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REVIEW 4 major objections 5 minor 76 references

Comparative performance of ensemble models in predicting dental provider types: insights from fee-for-service data

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read On a 2018 dental claims dataset, a three-layer neural network identifies safety-net providers with an AUC of 0.975, beating 11 other classifiers.

desk verdict A routine 12-classifier benchmark on a dental claims dataset whose headline NN/GB AUC numbers are not trustworthy as reported because preprocessing may leak across CV folds, and the paper's own tables contradict its feature-selection description. read the letter →

arxiv 2506.04479 v1 pith:ZA3UESOF submitted 2025-06-04 cs.LG cs.AI

Mohammad Subhi Al-Batah , Muhyeeddin Alqaraleh , Mowafaq Salem Alzboon , Abdullah Alourani This is my paper
classification cs.LGcs.AI
keywords dentalproviderclassificationsafetynetclinicsensemblemachinelearningneuralnetworksclassimbalanceSMOTEfee-for-serviceclaimsdatahealthcareanalytics
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to show that modern machine-learning models, especially ensembles and a small neural network, can classify dental providers from routine insurance-claims counts more accurately than traditional classifiers. Using 24,300 providers from 2018 fee-for-service and managed-care data, with 20 service-count and delivery-system features and 38.1% missing values, it compares 12 algorithms under 10-fold cross-validation. It reports that a neural network reaches the best discrimination (AUC $0.974958$, accuracy $0.941193$), followed by gradient boosting (AUC $0.970068$) and random forest (AUC $0.947992$), while logistic regression, decision trees, and SVM hover near chance. The authors argue this matters because reliably identifying safety-net clinic providers would let policymakers target funding and services to underserved populations. The paper presents the results as evidence that ensemble and deep models handle the dataset's class imbalance and nonlinear feature interactions better than traditional statistical models.

What carries the argument

The central object is a benchmark pipeline rather than a single identity: the 20 raw features are cleaned by median imputation for numeric and mode imputation for categorical values, rebalanced with SMOTE, reduced by PCA retaining 95% variance and by recursive feature elimination down to 12 predictors, and scored by 10-fold stratified cross-validation on AUC, accuracy, F1, precision, and recall. The winning model is a three-layer neural network (64-32-16 neurons, ReLU activation, Adam optimizer); the competing ensembles are random forest (200 trees, max depth 15) and gradient boosting. The pipeline does the work of making the comparison: it turns the raw claims table into a ranked table of twelve classifiers.

What would settle it

Rerun the 10-fold comparison with every preparation step — filling missing values, synthetic oversampling of the minority class, dimension reduction, and feature selection — computed inside each training fold only, and compare the neural network's AUC with the reported $0.974958$; if the gap against near-chance logistic regression narrows sharply, the original ranking was inflated by validation information leaking into training. The simplest observation that would settle it is the order of operations in the code: if oversampling or imputation runs before the cross-validation split, the headline numbers are not honest test-set estimates.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is a clean performance ranking for dental-provider classification on 2018 claims data: Neural Network leads with AUC $0.974958$ and classification accuracy $0.941193$, Gradient Boosting is close behind (AUC $0.970068$, CA $0.932099$), and Random Forest follows (AUC $0.947992$, CA $0.929753$). Traditional models do not compete: SVM reaches AUC $0.599065$, while Logistic Regression, Decision Tree, and the Constant classifier sit at about $0.50$. The paper attributes the top models' success to their ability to capture non-linear relationships and interactions among service counts, delivery system, and age group, aided by SMOTE for the 19.3% minority class and by median/mode imputation for the 38.1% missing data. The intended conclusion is that advanced ML, particularly ensemble and neural models, can be integrated into healthcare analytics to improve identification of safety-net clinic providers and thereby support equitable resource allocation.

Load-bearing premise

The headline accuracies depend on the assumption that all preparation steps that look at the data — filling missing values, balancing the classes with synthetic examples, and reducing the feature set — were learned only from the training part of each cross-validation fold and never from the validation fold; the methods section never states that this was done.

Editorial extensions

If this is right

  • If the ranking holds, a small neural network trained on claims counts can flag likely safety-net clinic providers automatically, without collecting new clinical data.
  • Health agencies could apply the same pipeline to other years' claims to monitor shifts in safety-net capacity and target funding accordingly.
  • The reported dominance of gradient boosting and random forest over logistic regression and SVM suggests that similar imbalanced, missing-data health administrative datasets are better served by ensemble methods.
  • The benchmark provides a reusable preprocessing recipe — imputation, SMOTE, PCA, and recursive feature elimination — for provider-level analytics.
  • Accurate SNC identification would make it possible to measure whether underserved populations are actually being reached, since provider type is the key variable in that analysis.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A natural extension the paper leaves implicit is temporal validation: retraining on 2018 data and testing on later years would show whether the 0.975 AUC is stable as delivery systems and claims coding change.
  • Because delivery system and age group appear among the 12 retained predictors, a follow-up could test whether the model is mainly separating providers by contractual plan type rather than by safety-net status per se.
  • The same pipeline could be transferred to other provider types, such as medical or behavioral health providers, as long as the target definition is tied to a claims-visible designation.
  • A reader who wants to deploy the model in a new state should first verify that the publicly posted 2018 sample matches local claims structure, since no external validation is reported.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. This paper evaluates 12 machine learning algorithms for classifying dental providers as standard rendering providers or safety net clinic (SNC) providers, using a 2018 fee-for-service dataset of 24,300 instances with 20 features and 38.1% missing values. The authors report that Neural Networks achieve the highest AUC (0.974958) and accuracy (0.941193), followed by Gradient Boosting and Random Forest, and conclude that ensemble and deep learning models are superior to traditional classifiers such as Logistic Regression and SVM. The paper also describes preprocessing steps including median/mode imputation, SMOTE, PCA, and recursive feature elimination, and discusses policy implications for healthcare resource allocation.

Significance. If the reported results were trustworthy, the paper would offer a useful empirical comparison of ML models on a real-world dental claims dataset, particularly in a setting with substantial missing data and class imbalance. The study addresses a topic with limited prior work and has potential practical relevance for dental workforce planning. However, the analysis as presented has several load-bearing methodological gaps that prevent the results from being accepted as evidence for the central claim. The paper does not establish that the preprocessing pipeline is free of data leakage, does not provide code or data for verification, and includes degenerate baselines that make the traditional-versus-advanced comparison trivial. The potential contribution is therefore conditional on a substantial re-analysis.

major comments (4)
  1. [§Method (preprocessing) and Table 1] The Methods section describes median/mode imputation, SMOTE, PCA (95% variance), and recursive feature elimination (12 predictors) before introducing the 10-fold cross-validation framework, and it never states that these steps are nested inside each training fold. If any of these steps are fit on the full dataset, information from validation folds leaks into training, which would inflate the reported AUC and accuracy. This is a load-bearing premise for the paper's headline result. The contradiction with Table 1's caption, which says 'using all 20 features,' while the Methods say RFE selected 12 predictors, further makes the reported scores uninterpretable. The authors must clarify the exact pipeline and re-run all experiments with preprocessing performed inside the cross-validation loop.
  2. [Table 1 (Tree, Logistic Regression, Constant)] Tree, Logistic Regression, and Constant have identical CA (0.810617), F1 (0.72583), precision (0.6571), and recall (0.810617), and Tree and Constant share AUC 0.499786. These are exactly the metrics of a constant classifier that always predicts the majority class, since 19,698/24,300 = 0.810617. This indicates that Tree and Logistic Regression are degenerate majority-class predictors and are not actually learning from the data. The paper's claim that these 'traditional models' perform poorly is therefore a comparison against a trivial baseline, not evidence of any substantive limitation of these algorithms. The authors should verify their implementations and report properly trained baselines with class-weight adjustments or hyperparameter tuning.
  3. [§Method (feature list)] The list of 20 features includes 'Rendering npi' (a provider identifier) and 'calendar year' (constant, since all data are from 2018). Using a provider identifier as a predictive feature can allow the model to memorize provider-level outcomes, especially when the same NPI appears in both training and test folds; this constitutes a direct leakage channel that could explain the exceptionally high performance of flexible models like Neural Networks. The authors should exclude identifier and constant columns from the feature set and re-run the experiments to assess the true contribution of the remaining features.
  4. [Results and reproducibility] All performance scores are reported as point estimates without confidence intervals, repeated cross-validation, or statistical significance tests, so the ranking among the top models (NN AUC 0.974958 vs. Gradient Boosting 0.970068 vs. Random Forest 0.947992) cannot be assessed for reliability. No code or data are provided, and the text refers to 'Figure 1' and 'Figure 2' for ROC curves that are not actually included, making the results unverifiable. The authors should provide all materials and include uncertainty quantification, or the reported differences should not be interpreted as meaningful.
minor comments (5)
  1. [Results after reference 61] The Results section contains an irrelevant fragment: 'which are: the server, network configuration and clients. The clients are varied from numerous approaches, battery capacities...' that appears to be copied from another manuscript; this should be removed.
  2. [Abstract vs. Methods] The abstract lists only seven algorithms, while the Methods section states that twelve algorithms were evaluated (including Constant, CN2 rule inducer, and AdaBoost); the counts should be made consistent.
  3. [Global text formatting] The manuscript inconsistently uses both '38,1 %' and '38.1%' and mixes decimal comma and decimal point styles; please standardize.
  4. [Data availability] The dataset is described as sourced from Kaggle, but no link, version, or preprocessing script is provided; adding these would greatly improve reproducibility.
  5. [References] The introduction and related work cite a large number of the authors' own publications on topics unrelated to dental provider classification (e.g., UAVs, grid computing, phishing); these should be replaced or supplemented with directly relevant literature.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the benchmark results are empirical measurements, not derivations from the model definitions.

full rationale

The paper's central claims are direct empirical measurements from a 10-fold cross-validation comparison of twelve classifiers on a fixed dataset; there is no derivation chain in which an output is constructed from its own inputs. The reported AUC, CA, F1, precision, and recall values are measurements, not predictions derived from fitted parameters. The heavy self-citation in the introduction, related work, and methods (e.g., refs. 1-26, 42-46) is not load-bearing: the benchmark procedure and results do not depend on any of those cited results, and no cited prior work is invoked to forbid alternatives or to justify a uniqueness claim. Potential methodological problems—preprocessing (imputation, SMOTE, PCA, RFE) possibly fitted before cross-validation, degenerate constant-like baselines for Tree and Logistic Regression, and the unresolved 'all 20 features' vs. '12 critical predictors' discrepancy—are correctness or leakage concerns, not circularity in the sense of a claim reducing by construction to its own assumptions. No step of the paper's argument equates a prediction with a fitted input, nor does it import a result solely via self-citation. Therefore the circularity score is 0.

Assumptions & free parameters 6 free parameters · 4 assumptions · 0 invented entities

The central claim rests on a single dataset and a set of preprocessing choices whose order relative to cross-validation is unspecified. No new theoretical entities are introduced. The main free parameters are hyperparameters and preprocessing thresholds selected on the same data, plus an undisclosed SMOTE strategy.

free parameters (6)
  • Random Forest n_estimators / max_depth = 200 / 15
    Chosen by grid search on the same dataset; no sensitivity analysis.
  • Neural Network architecture = 64-32-16 nodes, ReLU, Adam
    Selected by grid search; no learning rate, regularization, or early stopping reported.
  • SMOTE oversampling strategy = not reported
    Oversampling ratio and algorithm settings are not given; this changes the training distribution and all metrics.
  • Imputation choice = median (numeric), mode (categorical)
    Chosen without comparison or sensitivity analysis; the stated missing-data impact analysis is not performed.
  • PCA variance threshold = 95%
    Ad hoc threshold; number of components not reported and results table says all 20 features were used.
  • RFE target feature count = 12
    Target of 12 predictors is not justified, and the reported results do not separate 12-feature from 20-feature runs.
assumptions (4)
  • domain assumption The dataset is a faithful representation of 2018 dental fee-for-service claims with reliable provider-type labels.
    All conclusions rest on the Kaggle dataset's provenance and label quality; no data dictionary or validation is provided.
  • ad hoc to paper Preprocessing steps (imputation, SMOTE, PCA, feature selection) are fitted only on training folds.
    The paper never states the order; if full-data statistics are used, evaluation is leaky.
  • standard math Evaluation folds consist of original, unaugmented samples.
    AUC and accuracy are only meaningful if test folds exclude synthetic SMOTE examples; the paper applies SMOTE but does not clarify.
  • ad hoc to paper Point estimates without confidence intervals are sufficient for model comparison.
    The paper draws conclusions from small differences (e.g., 0.002 in F1) without error bars or significance tests.

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Cite this review

Pith. "Pith review of Comparative performance of ensemble models in predicting dental provider types: insights from fee-for-service data." pith.science (2026). https://pith.science/paper/ZA3UESOF

@misc{pith2026250604479,
  author       = {Pith},
  title        = {Pith review of: Comparative performance of ensemble models in predicting dental provider types: insights from fee-for-service data},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZA3UESOF}},
  note         = {Machine review of arXiv:2506.04479}
}
read the original abstract

Dental provider classification plays a crucial role in optimizing healthcare resource allocation and policy planning. Effective categorization of providers, such as standard rendering providers and safety net clinic (SNC) providers, enhances service delivery to underserved populations. This study aimed to evaluate the performance of machine learning models in classifying dental providers using a 2018 dataset. A dataset of 24,300 instances with 20 features was analyzed, including beneficiary and service counts across fee-for-service (FFS), Geographic Managed Care, and Pre-Paid Health Plans. Providers were categorized by delivery system and patient age groups (0-20 and 21+). Despite 38.1% missing data, multiple machine learning algorithms were tested, including k-Nearest Neighbors (kNN), Decision Trees, Support Vector Machines (SVM), Stochastic Gradient Descent (SGD), Random Forest, Neural Networks, and Gradient Boosting. A 10-fold cross-validation approach was applied, and models were evaluated using AUC, classification accuracy (CA), F1-score, precision, and recall. Neural Networks achieved the highest AUC (0.975) and CA (94.1%), followed by Random Forest (AUC: 0.948, CA: 93.0%). These models effectively handled imbalanced data and complex feature interactions, outperforming traditional classifiers like Logistic Regression and SVM. Advanced machine learning techniques, particularly ensemble and deep learning models, significantly enhance dental workforce classification. Their integration into healthcare analytics can improve provider identification and resource distribution, benefiting underserved populations.

Figures

Figures reproduced from arXiv: 2506.04479 by the authors.

Figure 1
Figure 1. ROC analysis for target rendering Based on the provided ROC (Receiver Operating Characteristic) curve for the classification of dental providers into the category “RENDERING SNC” (Safety Net Clinic providers), here’s a detailed analysis: General Observations: • Target Class: The analysis focuses on the “RENDERING SNC” class, which constitutes 19,0 % of the target probability in the dataset. • Evaluation Metrics: The… view at source ↗
Figure 2
Figure 2. ROC analysis for target rendering SNC Lower Performing Models: • SGD (AUC = 0,730): With an AUC of 0,730, Stochastic Gradient Descent shows moderate performance, better than some but not as high as the ensemble methods or Neural Networks. • Naive Bayes (AUC = 0,733): Similar to SGD, with an AUC of 0,733, indicating it performs better than random guessing but not as well as the top models. • kNN (AUC = 0,727): k-Near… view at source ↗

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Works this paper leans on

76 extracted references · 26 canonical work pages

  1. [1]

    Application of Artificial Intelligence for Diagnosing Tumors in the Female Reproductive System: A Systematic Review

    Wahed MA, Alqaraleh M, Alzboon MS, Al-Batah MS. Application of Artificial Intelligence for Diagnosing Tumors in the Female Reproductive System: A Systematic Review. Multidiscip. 2025;3:54

    2025

  2. [2]

    Automated quantification of vesicoureteral reflux using machine learning with advancing diagnostic precision

    Alqaraleh M, Al-Batah M, Salem Alzboon M, Alzaghoul E. Automated quantification of vesicoureteral reflux using machine learning with advancing diagnostic precision. Data Metadata. 2025;4:460

    2025

  3. [3]

    Guardians of the Web: Harnessing Machine Learning to Combat Phishing Attacks

    Salem Alzboon M, Subhi Al-Batah M, Alqaraleh M, Alzboon F , Alzboon L. Guardians of the Web: Harnessing Machine Learning to Combat Phishing Attacks. Gamification Augment Real [Internet]. 2025 Jan;3:91. Available from: http://dx.doi.org/10.56294/gr202591

    doi:10.56294/gr202591 2025

  4. [4]

    From Complexity to Clarity: Data and Metadata

    Alqaraleh M, Salem Alzboon M, Subhi Al-Batah M, Solayman Migdadi H. From Complexity to Clarity: Data and Metadata. 2025; 4:750 10 Improving Microarray Classification with Correlation-Based Feature Selection. LatIA [Internet]. 2025 Jan 1;3:84. Available from: https://latia.ageditor.uy/index.php/latia/article/view/84

    work page 2025

  5. [5]

    Phishing Website Detection Using Machine Learning

    Alzboon MS, Subhi Al-Batah M, Alqaraleh M, Alzboon F , Alzboon L. Phishing Website Detection Using Machine Learning. Gamification Augment Real [Internet]. 2025 Jan 16;3:81. Available from: http://dx.doi. org/10.56294/gr202581

    work page doi:10.56294/gr202581 2025

  6. [6]

    Optimizing Resource Discovery in Grid Computing: A Hierarchical and Weighted Approach with Behavioral Modeling

    Alqaraleh M, Salem Alzboon M, Mohammad SA-B. Optimizing Resource Discovery in Grid Computing: A Hierarchical and Weighted Approach with Behavioral Modeling. LatIA [Internet]. 2025 Jan 1;3:97. Available from: https://latia.ageditor.uy/index.php/latia/article/view/97

    2025

  7. [7]

    Real-Time UAV Recognition Through Advanced Machine Learning for Enhanced Military Surveillance

    Alqaraleh M, Salem Alzboon M, Subhi Al-Batah M. Real-Time UAV Recognition Through Advanced Machine Learning for Enhanced Military Surveillance. Gamification Augment Real [Internet]. 2025 Jan 1;3:63. Available from: https://gr.ageditor.ar/index.php/gr/article/view/63

    2025

  8. [8]

    AI Rx: Revolutionizing Healthcare Through Intelligence, Innovation, and Ethics

    Wahed MA, Alqaraleh M, Alzboon MS, Subhi Al-Batah M. AI Rx: Revolutionizing Healthcare Through Intelligence, Innovation, and Ethics. Semin Med Writ Educ [Internet]. 2025 Jan 1;4:35. Available from: https:// mw.ageditor.ar/index.php/mw/article/view/35

    2025

Show all 76 references
  1. [9]

    Application of Artificial Intelligence for Diagnosing Tumors in the Female Reproductive System: A Systematic Review

    Abdel Wahed M, Alqaraleh M, Salem Alzboon M, Subhi Al-Batah M. Application of Artificial Intelligence for Diagnosing Tumors in the Female Reproductive System: A Systematic Review. Multidiscip [Internet]. 2025 Jan 1;3:54. Available from: https://multidisciplinar.ageditor.uy/ind...

    2025

  2. [10]

    Evaluating AI and Machine Learning Models in Breast Cancer Detection: A Review of Convolutional Neural Networks (CNN) and Global Research Trends

    Wahed MA, Alqaraleh M, Salem Alzboon M, Subhi Al-Batah M. Evaluating AI and Machine Learning Models in Breast Cancer Detection: A Review of Convolutional Neural Networks (CNN) and Global Research Trends. LatIA [Internet]. 2025 Jan 1;3:117. Available from: https://latia.agedito...

    2025

  3. [11]

    Comparative Study of Classification Mechanisms of Machine Learning on Multiple Data Mining Tool Kits

    Abuashour A, Salem Alzboon M, Kamel Alqaraleh M, Abuashour A. Comparative Study of Classification Mechanisms of Machine Learning on Multiple Data Mining Tool Kits. Am J Biomed Sci Res 2024 [Internet]. 2024;22(1):1. Available from: www.biomedgrid.com

    2024

  4. [12]

    AI in the Sky: Developing Real-Time UAV Recognition Systems to Enhance Military Security

    Mowafaq SA, Alqaraleh M, Al-Batah MS. AI in the Sky: Developing Real-Time UAV Recognition Systems to Enhance Military Security. Data Metadata. 2024;3:417

    2024

  5. [13]

    Skywatch: Advanced Machine Learning Techniques for Distinguishing UAVs from Birds in Airspace Security

    Alqaraleh M, Alzboon MS, Al-Batah MS. Skywatch: Advanced Machine Learning Techniques for Distinguishing UAVs from Birds in Airspace Security. Int J Adv Comput Sci Appl [Internet]. 2024;15(11). Available from: http:// dx.doi.org/10.14569/IJACSA.2024.01511104

    2024

  6. [19]

    Automating Web Data Collection: Challenges, Solutions, and Python-Based Strategies for Effective Web Scraping

    Wahed MA, Alzboon MS, Alqaraleh M, Ayman J, Al-Batah M, Bader AF . Automating Web Data Collection: Challenges, Solutions, and Python-Based Strategies for Effective Web Scraping. In: 2024 7th International Conference on Internet Applications, Protocols, and Services, NETAPPS 20...

    2024

  7. [20]

    Comparative Analysis of Advanced Data Mining Methods for Enhancing Medical Diagnosis and Prognosis

    Al-Batah M, Salem Alzboon M, Alqaraleh M, Ahmad Alzaghoul F . Comparative Analysis of Advanced Data Mining Methods for Enhancing Medical Diagnosis and Prognosis. Data Metadata. 2024;3:465

    2024

  8. [21]

    Enhancing Internet-based Resource Discovery: The Efficacy of Distributed Quadtree Overlay

    Alqaraleh M. Enhancing Internet-based Resource Discovery: The Efficacy of Distributed Quadtree Overlay. In: Proceedings of the 3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC

  9. [22]

    Enhanced Resource Discovery Algorithm for Efficient Grid Computing

    Alqaraleh M. Enhanced Resource Discovery Algorithm for Efficient Grid Computing. In: Proceedings of the 3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024. 2024. p. 925–31

    2024

  10. [23]

    Advanced Landslide Detection Using Machine Learning and Remote Sensing Data

    Al-Batah MS, Salem Alzboon M, Solayman Migdadi H, Alkhasawneh M, Alqaraleh M. Advanced Landslide Detection Using Machine Learning and Remote Sensing Data. Data Metadata [Internet]. 2024 Oct 7;1. Available from: https://dm.ageditor.ar/index.php/dm/article/view/419/782

    2024

  11. [24]

    Advanced ensemble machine learning techniques for optimizing diabetes mellitus prognostication: A detailed examination of hospital data

    Al-Shanableh N, Alzyoud M, Al-Husban RY , Alshanableh NM, Al-Oun A, Al-Batah MS, et al. Advanced ensemble machine learning techniques for optimizing diabetes mellitus prognostication: A detailed examination of hospital data. Data Metadata. 2024;3:363

    2024

  12. [25]

    Enhancing Image Cryptography Performance with Block Left Rotation Operations

    Al-Batah MS, Alzboon MS, Alzyoud M, Al-Shanableh N. Enhancing Image Cryptography Performance with Block Left Rotation Operations. Appl Comput Intell Soft Comput. 2024;2024(1):3641927

    2024

  13. [26]

    Harnessing Machine Learning for Quantifying Vesicoureteral Reflux: A Promising Approach for Objective Assessment

    Alqaraleh M, Alzboon MS, Al-Batah MS, Wahed MA, Abuashour A, Alsmadi FH. Harnessing Machine Learning for Quantifying Vesicoureteral Reflux: A Promising Approach for Objective Assessment. Int J online Biomed Eng. 2024;20(11):123–45

    2024

  14. [27]

    An artificial intelligence system using machine- learning for automatic detection and classification of dental restorations in panoramic radiography

    Abdalla-Aslan R, Yeshua T , Kabla D, Leichter I, Nadler C. An artificial intelligence system using machine- learning for automatic detection and classification of dental restorations in panoramic radiography. Oral Surg Oral Med Oral Pathol Oral Radiol. 2020;130(5):593–602

    2020

  15. [28]

    A Comparative Analysis of Deep Learning-Based Approaches for Classifying Dental Implants Decision Support System

    Lubbad MAH, Kurtulus IL, Karaboga D, Kilic K, Basturk A, Akay B, et al. A Comparative Analysis of Deep Learning-Based Approaches for Classifying Dental Implants Decision Support System. J Imaging Informatics Med. 2024

    2024

  16. [29]

    Advancements in Dental Diagnostics: YOLOv3-Powered Machine Learning for Automated Teeth Problem Detection

    Odeh S, Samara A, Zreineh N, Obeid S, Ibrieghieth M, Obaid M. Advancements in Dental Diagnostics: YOLOv3-Powered Machine Learning for Automated Teeth Problem Detection. In: 2023 24th International Arab Conference on Information Technology, ACIT 2023. 2023

    2023

  17. [30]

    Trajectories of Dental Caries From Childhood to Young Adulthood: Unsupervised Machine Learning Approach

    Ogwo C, Levy S, Warren J, Caplan D, Brown G. Trajectories of Dental Caries From Childhood to Young Adulthood: Unsupervised Machine Learning Approach. Res Sq [Internet]. 2023; Available from: http://www.ncbi. nlm.nih.gov/pubmed/37546769%0Ahttp://www.pubmedcentral.nih.gov/articl...

    2023

  18. [31]

    Forecasting of the Dental Workforce with Machine Learning Models

    Atalan A, Şahin H. Forecasting of the Dental Workforce with Machine Learning Models. Mühendislik Bilim ve Araştırmaları Derg. 2024;6(1):125–32

    2024

  19. [32]

    A Machine Learning Approach for Determination of Gender of Sri Lankan People by Using the Dental Arch Dimensions

    Mahanayake KMLK, Nawarathna LS, Arambawatta AKS, Abeysundara RGAP . A Machine Learning Approach for Determination of Gender of Sri Lankan People by Using the Dental Arch Dimensions. In: ICARC 2024 - 4th International Conference on Advanced Research in Computing: Smart and Inno...

    2024

  20. [33]

    Developing an AI-Powered Algorithm for Automated Detection and Classification of Dental Data and Metadata

    Haider M. Developing an AI-Powered Algorithm for Automated Detection and Classification of Dental Data and Metadata. 2025; 4:750 12 Caries from Intraoral Radiographs: A Machine Learning Approach. J Pharm Bioallied Sci [Internet]. 2024;16(Suppl 4):S3089–91. Available from: http...

    2025

  21. [34]

    Automated machine learning for image-based detection of dental plaque on permanent teeth

    Nantakeeratipat T , Apisaksirikul N, Boonrojsaree B, Boonkijkullatat S, Simaphichet A. Automated machine learning for image-based detection of dental plaque on permanent teeth. Front Dent Med. 2024;5

    2024

  22. [35]

    Model Proposal for Equity Production in Access to Dental Specialty Centers

    Filho JMM, da Silva Ferreira MA, Monteiro DLA, de Nazaré Wanderley Lira G, de Araújo TP , de Moraes Ramalho AKB, et al. Model Proposal for Equity Production in Access to Dental Specialty Centers. Pesqui Bras Odontopediatria Clin Integr. 2024;24

    2024

  23. [36]

    Dental Age Estimation in Northern Chinese Han Children and Adolescents Using Demirjian’s Method Combined with Machine Learning Algorithms

    Guo YX, Bu WQ, Tang Y , Wu D, Yang H, Meng HT , et al. Dental Age Estimation in Northern Chinese Han Children and Adolescents Using Demirjian’s Method Combined with Machine Learning Algorithms. J Forensic Med. 2024;40(2):135–42

    2024

  24. [37]

    Study on Detecting the Teeth and Classifying the Teeth Structure using Machine Learning and CNN

    Anusha N, Ashwini B, Venugopala PS. Study on Detecting the Teeth and Classifying the Teeth Structure using Machine Learning and CNN. In: 2024 IEEE 9th International Conference for Convergence in Technology, I2CT 2024. 2024

    2024

  25. [38]

    Using a machine learning algorithm to predict the likelihood of presence of dental caries among children aged 2 to 7

    Ramos-Gomez F , Marcus M, Maida CA, Wang Y , Kinsler JJ, Xiong D, et al. Using a machine learning algorithm to predict the likelihood of presence of dental caries among children aged 2 to 7. Dent J. 2021;9(12)

    2021

  26. [39]

    Application of Machine Learning For Prediction Dental Material Wear

    SURYAWANSHI A, BEHERA N. Application of Machine Learning For Prediction Dental Material Wear. J Polym Mater. 2024;40(3–4):305–16

    2024

  27. [40]

    Development of short forms for screening children’s dental caries and urgent treatment needs using item response theory and machine learning methods

    Xiong D, Marcus M, Maida CA, Lyu Y , Hays RD, Wang Y , et al. Development of short forms for screening children’s dental caries and urgent treatment needs using item response theory and machine learning methods. PLoS One. 2024;19(3 March)

    2024

  28. [41]

    Novel applications of artificial intelligence, machine learning, and deep learning-based modalities in dental traumatology: An overview of evidence-based literature

    Khan MK. Novel applications of artificial intelligence, machine learning, and deep learning-based modalities in dental traumatology: An overview of evidence-based literature. MRIMS J Heal Sci. 2024

    2024

  29. [42]

    Advancing Medical Image Analysis: The Role of Adaptive Optimization Techniques in Enhancing COVID-19 Detection, Lung Infection, and Tumor Segmentation

    Muhyeeddin A, Mowafaq SA, Al-Batah MS, Mutaz AW. Advancing Medical Image Analysis: The Role of Adaptive Optimization Techniques in Enhancing COVID-19 Detection, Lung Infection, and Tumor Segmentation. LatIA [Internet]. 2024 Sep 29;2(74):74. Available from: https://latia.agedit...

    2024

  30. [43]

    Doctor Adam Talib’s Public Relations Strategy in Improving the Quality of Patient Service

    Putri AK, Alzboon MS. Doctor Adam Talib’s Public Relations Strategy in Improving the Quality of Patient Service. Sinergi Int J Commun Sci. 2023;1(1):42–54

    2023

  31. [44]

    Intelligent Heart Disease Prediction System with Applications in Jordanian Hospitals

    Al-Batah MS, Alzboon MS, Alazaidah R. Intelligent Heart Disease Prediction System with Applications in Jordanian Hospitals. Int J Adv Comput Sci Appl. 2023;14(9):508–17

    2023

  32. [45]

    Prostate Cancer Detection and Analysis using Advanced Machine Learning

    Alzboon MS, Al-Batah MS. Prostate Cancer Detection and Analysis using Advanced Machine Learning. Int J Adv Comput Sci Appl. 2023;14(8):388–96

    2023

  33. [46]

    The Two Sides of AI in Cybersecurity: Opportunities and Challenges

    Alzboon MS, Bader AF , Abuashour A, Alqaraleh MK, Zaqaibeh B, Al-Batah M. The Two Sides of AI in Cybersecurity: Opportunities and Challenges. In: Proceedings of 2023 2nd International Conference on Intelligent Computing and Next Generation Networks, ICNGN 2023. 2023

    2023

  34. [47]

    A Comparative Study of Machine Learning Techniques for Early Prediction of Diabetes

    Alzboon MS, Al-Batah M, Alqaraleh M, Abuashour A, Bader AF . A Comparative Study of Machine Learning Techniques for Early Prediction of Diabetes. In: 2023 IEEE 10th International Conference on Communications and Networking, ComNet 2023 - Proceedings. 2023. p. 1–12

    2023

  35. [48]

    A Comparative Study of Machine Learning Techniques for Early Prediction of Prostate Cancer

    Alzboon MS, Al-Batah M, Alqaraleh M, Abuashour A, Bader AF . A Comparative Study of Machine Learning Techniques for Early Prediction of Prostate Cancer. In: 2023 IEEE 10th International Conference on Communications and Networking, ComNet 2023 - Proceedings. 2023. p. 1–12

    2023

  36. [50]

    Early Diagnosis of Diabetes: A Comparison of Machine Learning Methods

    Alzboon MS, Al-Batah MS, Alqaraleh M, Abuashour A, Bader AFH. Early Diagnosis of Diabetes: A Comparison of Machine Learning Methods. Int J online Biomed Eng. 2023;19(15):144–65

    2023

  37. [51]

    Pushing the Envelope: Investigating the Potential and Limitations of ChatGPT and Artificial Intelligence in Advancing Computer Science Research

    Alzboon MS, Qawasmeh S, Alqaraleh M, Abuashour A, Bader AF , Al-Batah M. Pushing the Envelope: Investigating the Potential and Limitations of ChatGPT and Artificial Intelligence in Advancing Computer Science Research. In: 2023 3rd International Conference on Emerging Smart Tec...

    2023

  38. [52]

    Survey on Patient Health Monitoring System Based on Internet of Things

    Alzboon MS. Survey on Patient Health Monitoring System Based on Internet of Things. Inf Sci Lett. 2022;11(4):1183–90

    2022

  39. [53]

    Semantic Text Analysis on Social Networks and Data Processing: Review and Future Directions

    Alzboon M. Semantic Text Analysis on Social Networks and Data Processing: Review and Future Directions. Inf Sci Lett. 2022;11(5):1371–84

    2022

  40. [54]

    Nodexl Tool for Social Network Analysis

    Alzboon MS, Aljarrah E, Alqaraleh M, Alomari SA. Nodexl Tool for Social Network Analysis. Turkish J Comput Math Educ. 2021;12(14):202–16

    2021

  41. [55]

    Enhanced logistics information service systems performance: using theoretical model and cybernetics’ principles

    Alomari SA, Salaimeh S Al, Jarrah E Al, Alzboon MS. Enhanced logistics information service systems performance: using theoretical model and cybernetics’ principles. WSEAS Trans Bus Econ [Internet]. 2020 Apr;17:278–87. Available from: https://wseas.com/journals/bae/2020/a585107-896.pdf

    2020

  42. [56]

    A novel adaptive schema to facilitates playback switching technique for video delivery in dense LTE cellular heterogeneous network environments

    Alomari SA, Alzboon MS, Al-Batah MS, Zaqaibeh B. A novel adaptive schema to facilitates playback switching technique for video delivery in dense LTE cellular heterogeneous network environments. Int J Electr Comput Eng [Internet]. 2020 Oct;10(5):5347. Available from: http://ije...

    2020

  43. [57]

    Toward achieving self-resource discovery in distributed systems based on distributed quadtree

    Alomari SA, Alqaraleh M, Aljarrah E, Alzboon MS. Toward achieving self-resource discovery in distributed systems based on distributed quadtree. J Theor Appl Inf Technol. 2020;98(20):3088–99

    2020

  44. [58]

    Resource discovery mechanisms in shared computing infrastructure: A survey

    Alzboon MS, Mahmuddin M, Arif S. Resource discovery mechanisms in shared computing infrastructure: A survey. In: Advances in Intelligent Systems and Computing. 2020. p. 545–56

    2020

  45. [59]

    Evaluation of knowledge quality in the E -learning system

    Shawawreh S, Alomari SA, Alzboon MS, Al Salaimeh S. Evaluation of knowledge quality in the E -learning system. Int J Eng Res Technol. 2019;12(4):548–53

    2019

  46. [60]

    An Effective Self-Adaptive Policy for Optimal Video Quality over Heterogeneous Mobile Devices and Network Discovery Services

    Alomari, Alzboon, Zaqaibeh, Al-Batah, Saleh Ali, Mowafaq Salem, Belal MS. An Effective Self-Adaptive Policy for Optimal Video Quality over Heterogeneous Mobile Devices and Network Discovery Services. Appl Math Inf Sci [Internet]. 2019 May;13(3):489–505. Available from: http://...

    2019

  47. [61]

    An advanced emergency warning message scheme based on vehicles speed and traffic densities

    Banikhalaf M, Alomari SA, Alzboon MS. An advanced emergency warning message scheme based on vehicles speed and traffic densities. Int J Adv Comput Sci Appl. 2019;10(5):201–5

    2019

  48. [62]

    Internet of things between reality or a wishing - list : a survey

    Alzboon MS. Internet of things between reality or a wishing - list : a survey. Int J Eng \& Technol. 2019;7(June):956–61

    2019

  49. [63]

    Gene Microarray Cancer classification using correlation based feature selection algorithm and rules classifiers

    Al-Batah M, Zaqaibeh B, Alomari SA, Alzboon MS. Gene Microarray Cancer classification using correlation based feature selection algorithm and rules classifiers. Int J online Biomed Eng. 2019;15(8):62–73

    2019

  50. [64]

    The modern hosting computing systems for small and medium businesses

    Al Tal S, Al Salaimeh S, Ali Alomari S, Alqaraleh M. The modern hosting computing systems for small and medium businesses. Acad Entrep J. 2019;25(4):1–7

    2019

  51. [65]

    Alzboon MS, Alomari S, Al-Batah MS, Alomari SA, Banikhalaf M. The characteristics of the green internet of things and big data in building safer, smarter, and sustainable cities Vehicle Detection and Tracking for Aerial Surveillance Videos View project Evaluation of Knowledge ...

    2017

  52. [66]

    Distributed quadtree overlay for resource discovery in shared computing infrastructure

    Arif S, Alzboon MS, Mahmuddin M. Distributed quadtree overlay for resource discovery in shared computing infrastructure. Adv Sci Lett. 2017;23(6):5397–401

    2017

  53. [67]

    Dynamic network topology for resource discovery in shared computing infrastructure

    Mahmuddin M, Alzboon MS, Arif S. Dynamic network topology for resource discovery in shared computing infrastructure. Adv Sci Lett. 2017;23(6):5402–5

    2017

  54. [68]

    Mahmuddin ASCA

    Mowafaq Salem Alzboon M. Mahmuddin ASCA. Challenges and Mitigation Techniques of Grid Resource Management System. In: National Workshop on FUTURE INTERNET RESEARCH (FIRES2016). 2016. p. 1–6

    2016

  55. [69]

    Towards autonomic overlay self-load balancing

    Al-Oqily I, Alzboon M, Al-Shemery H, Alsarhan A. Towards autonomic overlay self-load balancing. In: 2013 10th International Multi-Conference on Systems, Signals and Devices, SSD 2013. Ieee; 2013. p. 1–6

    2013

  56. [70]

    Diabetes Prediction and Management Using Machine Learning Approaches

    Alzboon MS, Alqaraleh M, Al-Batah MS. Diabetes Prediction and Management Using Machine Learning Approaches. Data Metadata [Internet]. 2025; Available from: https://doi.org/10.56294/dm2025545

    2025 doi

  57. [71]

    Echoes in the Genome: Smoking’s Epigenetic Fingerprints and Bidirectional Neurobiological Pathways in Addiction and Disease

    Alqaraleh M, Al-Batah MS, Alzboon MS, Alzboon F , Alzboon L, Alamoush MN. Echoes in the Genome: Smoking’s Epigenetic Fingerprints and Bidirectional Neurobiological Pathways in Addiction and Disease. Semin Med Writ Educ [Internet]. 2025; Available from: https://doi.org/10.56294...

    2025 doi

  58. [72]

    Ranked features selection with MSBRG algorithm and rules classifiers for cervical cancer

    Al-Batah MS. Ranked features selection with MSBRG algorithm and rules classifiers for cervical cancer. Int J Online Biomed Eng. 2019;15(12):4

    2019

  59. [73]

    Integrating the principal component analysis with partial decision tree in microarray gene data

    Al-Batah MS. Integrating the principal component analysis with partial decision tree in microarray gene data. IJCSNS Int J Comput Sci Netw Secur. 2019;19(3):24-29

    2019

  60. [74]

    Peer to Peer Resource Discovery Mechanisms in Grid Computing : A Critical Review

    SalemAlzboon, Mowafaq and Arif, Suki and Mahmuddin, M and Dakkak O. Peer to Peer Resource Discovery Mechanisms in Grid Computing : A Critical Review. In: The 4th International Conference on Internet Applications, Protocols and Services (NETAPPS2015). 2015. p. 48–54

    2015

  61. [75]

    An improved binary crow-JAYA optimisation system with various evolution operators, such as mutation for finding the max clique in the dense graph

    Al-Batah MS, Al-Eiadeh MR. An improved binary crow-JAYA optimisation system with various evolution operators, such as mutation for finding the max clique in the dense graph. Int J Comput Sci Math. 2024;19(4):327- 38

    2024

  62. [76]

    An improved discreet Jaya optimisation algorithm with mutation operator and opposition-based learning to solve the 0-1 knapsack problem

    Al-Batah MS, Al-Eiadeh MR. An improved discreet Jaya optimisation algorithm with mutation operator and opposition-based learning to solve the 0-1 knapsack problem. Int J Math Oper Res. 2023;26(2):143-69

    2023

  63. [77]

    From Puffs to Predictions: Leveraging AI, Wearables, and Biomolecular Signatures to Decode Smoking’s Multidimensional Impact on Cardiovascular Health

    Alqaraleh M, Al-Batah MS, Alzboon MS, Alzboon F , Alzboon L, Alamoush MN. From Puffs to Predictions: Leveraging AI, Wearables, and Biomolecular Signatures to Decode Smoking’s Multidimensional Impact on Cardiovascular Health. Semin Med Writ Educ [Internet]. 2025; Available from...

    2025

  64. [78]

    Testing the probability of heart disease using classification and regression tree model

    Al-Batah MS. Testing the probability of heart disease using classification and regression tree model. Annu Res Rev Biol. 2014;4(11):1713-25

    2014

  65. [79]

    Towards self-resource discovery and selection models in grid computing

    Alzboon MS, Arif AS, Mahmuddin M. Towards self-resource discovery and selection models in grid computing. ARPN J Eng Appl Sci. 2016;11(10):6269–74

    2016

  66. [80]

    A Comparative Study on Deep Learning and Machine Learning Models for Human Action Recognition in Aerial Videos

    Kapoor S, Sharma A, Verma A, Dhull V , Goyal C. A Comparative Study on Deep Learning and Machine Learning Models for Human Action Recognition in Aerial Videos. Int Arab J Inf Technol [Internet]. 2023;20(4)

    2023

  67. [81]

    Modified recursive least squares algorithm to train the hybrid multilayered perceptron (HMLP) network

    Al-Batah MS. Modified recursive least squares algorithm to train the hybrid multilayered perceptron (HMLP) network. Appl Soft Comput. 2010;10(1):236-44

    2010

  68. [82]

    Towards Self-Organizing Infrastructure : A New Architecture for Autonomic Green Cloud Data Centers

    Alzboon MS, Sintok UUM, Sintok UUM, Arif S. Towards Self-Organizing Infrastructure : A New Architecture for Autonomic Green Cloud Data Centers. ARPN J Eng Appl Sci. 2015;1–7. FINANCING This work is supported from Jadara University, Zarqa University, and Qassim University. http...

    2015 doi

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.