Pith. sign in

REVIEW 4 major objections 4 minor 78 references

Predicting Blood Type: Assessing Model Performance with ROC Analysis

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

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read This paper reports that fingerprint pattern categories — loops, whorls, arches — show no statistically significant correlation with ABO blood groups, implying fingerprints alone cannot predict blood type.

desk verdict This manuscript is internally contradictory and lacks a verifiable data foundation; it should be desk-rejected, not sent out. read the letter →

arxiv 2506.02062 v1 pith:ATHN4PWA submitted 2025-06-01 cs.LG cs.AI

Malik A. Altayar , Muhyeeddin Alqaraleh , Mowafaq Salem Alzboon , Wesam T. Almagharbeh This is my paper
classification cs.LGcs.AI
keywords BiometricsFingerprintPatternsABOBloodGroupForensicSciencePersonalIdentificationCorrelationAnalysisROCChi-squaretest
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

This paper tests an old forensic conjecture: that the coarse pattern of a person's fingerprints — loop, whorl, or arch — carries information about their ABO blood group. On a sample of 200 participants, the authors classified fingerprints, recorded blood groups, and ran chi-square and Pearson correlation analyses. They report p-values above 0.05 for the association, meaning the two traits behave as independent in this sample. If the result holds, it undercuts any simple use of fingerprint pattern classes as a noninvasive blood-type predictor and redirects the field toward multi-modal biometrics and image-based machine learning.

What carries the argument

The load-bearing device is the chi-square test of independence applied to the contingency table crossing three fingerprint pattern classes with ABO/Rh blood groups, supplemented by a Pearson correlation coefficient. The chi-square test asks whether the observed counts in each fingerprint–blood-group cell differ from the counts expected under independence; the reported p > 0.05 is what carries the conclusion that the traits are independent.

What would settle it

Obtain the paper's underlying 200-person contingency table of fingerprint patterns against ABO/Rh blood groups and rerun the chi-square test; observing p < 0.05, or finding that the table cannot be reconstructed from the described data, would overturn the claim.

Watch

Extended reading notes

Core claim

On its own terms, the paper's central claim is a null result: fingerprint pattern type (loops, whorls, arches) and ABO blood group are statistically independent in the studied population, with no significant correlation found. Loops were the most common pattern and O+ the most common blood group, matching broad population trends, but the contingency-table analysis did not reject independence. The paper also presents ROC curves for machine-learning classifiers aimed at predicting blood groups from fingerprint images, framing the null statistical finding as a caution against expecting coarse pattern classes to be predictive and as a motivation for richer biometric approaches.

Load-bearing premise

The result stands on the unverified premise that the 200-participant fingerprint and blood-group dataset is real and that the chi-square and Pearson analyses were actually computed from it; if the data are not as described, the independence conclusion has no support.

Editorial extensions

If this is right

  • Forensic examiners should not treat loop, whorl, or arch class as evidence about a person's ABO blood group.
  • Fingerprint pattern and blood group data should not be combined as correlated biometric signals; the paper argues instead for multi-modal systems in which traits contribute independently.
  • Larger, more diverse samples and machine-learning models are needed before any predictive use of fingerprint–blood group links is justified.
  • The null result reinforces calls for standardized fingerprint classification protocols so that future studies can be compared directly.
  • If the independence result holds, simple fingerprint-pattern-based blood typing kits would offer no better than chance performance on the coarse pattern categories.

Reading between the lines

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

  • If the independence result holds, any classifier trained only on the three coarse fingerprint-pattern categories should perform at chance for ABO group; high accuracy on fingerprint images would have to come from fine ridge details or dataset artifacts, not from pattern category.
  • A decisive extension would be a preregistered meta-analysis pooling the published contingency tables from prior fingerprint–blood group studies; if the combined p-value is also above 0.05, the independence claim would be much stronger.
  • The paper's ROC material suggests a shift toward a different question — whether fingerprint images encode blood type — which is logically independent of whether coarse pattern classes correlate with blood groups.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 4 minor

Summary. The manuscript claims to investigate the relationship between fingerprint patterns (loops, whorls, arches) and ABO blood groups in 200 participants, reporting chi-square and Pearson analyses with a non-significant result (p > 0.05). At the same time, it contains a Dataset Description referring to ~6000 rows and 8 blood group classes, plus six ROC figures claiming high AUC values (above 0.9) for six blood group classes. The abstract concludes that fingerprint patterns and blood groups are independent and that future work should use machine learning and larger samples.

Significance. If the null result were rigorously supported, it would be a modest but useful contribution to the dermatoglyphics literature, where prior findings are inconsistent. The manuscript, however, does not provide a verifiable evidentiary basis for its central claim: the reported sample size, dataset description, and ROC results are mutually contradictory, and the methodology section is an incomplete template with bracketed placeholders. The paper ships no data, no contingency table, no test statistics, no model code, and no reproducible analysis. As presented, the manuscript cannot support any of its conclusions about correlation or predictive performance.

major comments (4)
  1. [Abstract vs. Dataset Description] The abstract states that the study analyzed 200 individuals with three fingerprint pattern categories and found no significant correlation (p > 0.05). In direct contradiction, the Dataset Description states 'Total Instances: ~6000 data points' and 'Target Variable: Categorical outcome with 8 distinct blood group classes', with BMP image metadata. These two descriptions cannot describe the same study; the central statistical claim is therefore not anchored to a single, consistent dataset.
  2. [Methodology, Statistical Analysis] The methodology is an unfinished template: it contains placeholders such as '[specify population details]', '[specify the method used for power analysis, e.g., G*Power software]', '[specify fingerprint acquisition method]', '[specify statistical software, e.g., SPSS, R]', and '[Institutional Review Board (IRB) name]'. No contingency table, chi-square statistic, degrees of freedom, or exact p-value is reported anywhere. Consequently, the abstract's 'p > 0.05' claim cannot be checked or reproduced from the manuscript.
  3. [Figures 1–6] The ROC figure descriptions report high discriminative ability (AUC likely above 0.9) for target classes A+, A-, AB+, AB-, B+, and B-, based on 'target probabilities' of 9%, 17%, 12%, 13%, 11%, and 12% respectively. No model training procedure, train/test split, cross-validation, or out-of-sample evaluation is described anywhere. These curves directly contradict the abstract's conclusion of independence between fingerprints and blood groups. Moreover, the listed target probabilities do not sum to 100% and omit O+ and O- entirely, which is inconsistent with an 8-class classification problem.
  4. [Research Objectives] The Research Objectives section contains an unrelated bullet: 'Evaluate the impact of variables such as gender, blood group and fingerprint patterns on lip print patterns.' This is not aligned with the stated research objectives on fingerprint patterns and ABO blood groups, and it suggests that the section was assembled from unrelated source material. Such an inconsistency further undermines confidence in the manuscript's internal coherence.
minor comments (4)
  1. [References and citations] The reference list is heavily populated with self-citations and unrelated machine-learning papers (e.g., references 1–68), many of which are not cited in a meaningful way in the text. For instance, the introduction sentence ending with 'Dr Li concluded.(25)' cites a public relations case study that is unrelated to the topic.
  2. [Introduction, paragraph 7] The sentence 'Miniaturisation of biological machinery, engineered specificity and tuning inhibitors to more efficiently target uniquely fertilised cells can help overcome these limitations' is not meaningful in the context of fingerprint–blood-group correlation studies and appears to be extraneous or garbled text.
  3. [Related Work] There is a typo in the phrase 'The study subjects gave fingerparint by the printing method' — 'fingerparint' should be 'fingerprint'. Also, the related-work paragraphs are essentially summaries of other studies without synthesis or clear relevance to the current methodology.
  4. [Figures 1–6] The ROC figure descriptions are repetitive and speculative, repeatedly stating that 'the red and blue curves ... likely represent top-performing models like Neural Networks, SVM, or Logistic Regression' without reporting actual model names, AUC values, or threshold operating points. The figures themselves are not reproducible from the text.

Circularity Check

1 steps flagged · score 4.0 of 10

No derivation-level circularity found; the manuscript's claims are unsupported, and one load-bearing motivation step relies on irrelevant self-citations rather than a circular reduction of the result.

  1. self citation load bearing [Introduction, paragraph 5 (citations 15–17)]
    "Fingerprint patterns and their association with ABO blood groups has been the subject of several studies, each attempting to establish statistical significance. Some studies have found higher prevalence of loop patterns in individuals with blood group O, while others observed an increased frequency of whorl patterns in individuals with blood group B; however, these studies have been inconsistent due to the differences in populations and methodologies."

    The cited items [15] (Automating Web Data Collection), [16] (Challenges and Mitigation Techniques of Grid Resource Management), and [17] (Classifying Psychiatric Patients Using Machine Learning) are all self-authored by the present authors and do not report fingerprint–blood-group association studies. The premise that prior studies exist and are inconsistent is therefore not established by the cited evidence; it is imported from the authors' own unrelated bibliography rather than from independent literature. This step is load-bearing because the Introduction uses this claimed inconsistency to define the research gap and motivate the study's objectives.

full rationale

No true circular derivation was identified: the abstract's p > 0.05 null result, the descriptive frequencies, and the ROC-based performance statements are not derived from any equations or fitted parameters reported in the manuscript. The methodology is an incomplete template with bracketed placeholders such as '[specify population details]' and '[specify statistical software]'; no contingency table, chi-square statistic, degrees of freedom, or exact p-value is reported anywhere. The dataset section contradicts the stated 200-participant sample by describing approximately 6000 rows and 8 blood group classes, and the ROC figures are described as 'likely' evaluations with 'target probabilities' that resemble class priors, but no training/validation split or model predictions are provided. These are severe verification and correctness failures, not circular reductions: there is no derivation chain whose output is presupposed as an input. The one bibliographic step identified above is a load-bearing self-citation, which warrants a modest circularity-related score, but the central claim itself does not reduce to its inputs by construction.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The paper introduces no formal derivation. The free parameters are arbitrary cost weights, unverified class priors, and an unspecified sample size. The axioms include assumptions about data existence, classification reliability, and validity of statistical tests. The dataset inconsistency (200 vs ~6000 rows) is treated as an unexamined premise.

free parameters (3)
  • Cost weights FP and FN = 500 (both)
    In the ROC discussion, equal cost weights of 500 for false positives and false negatives are assumed without justification; these arbitrary values influence the recommended operating point. Source: Dataset Description / Figure 1-6 text.
  • Class target probabilities = A+ 9%, A- 17%, AB+ 12%, AB- 13%, B+ 11%, B- 12%
    Each ROC figure cites a 'target probability' for the class, but no source or connection to the 200-participant study is given; they appear ad hoc.
  • Sample size (200 participants) = 200
    The abstract and methodology state a sample of 200 chosen based on a power analysis, but the power analysis method is not specified and the dataset description later refers to ~6000 rows.
assumptions (4)
  • domain assumption Fingerprint classification into loops, whorls, and arches is reliable and standard.
    The methodology invokes 'established dermatoglyphic principles' but does not report inter-rater reliability or classification validation. Source: Methodology, 'Fingerprint Pattern Classification'.
  • domain assumption Chi-square and Pearson correlation are valid for these nominal categorical variables.
    The paper uses Pearson correlation on fingerprint pattern categories and blood groups, which are nominal, not continuous; this is statistically questionable. Source: Methodology, 'Statistical Analysis'.
  • ad hoc to paper The reported data were actually collected and analyzed.
    Methods contain placeholder text such as '[specify population details]' and no raw results are shown, so the existence of the dataset is an unverified premise. Source: Methodology section.
  • domain assumption The ROC figures pertain to the same study and dataset.
    The ROC descriptions mention target probabilities and a ~6000-row image dataset, contradicting the 200-participant design; the paper assumes these belong together. Source: Dataset Description and Figures 1-6.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Predicting Blood Type: Assessing Model Performance with ROC Analysis." pith.science (2026). https://pith.science/paper/ATHN4PWA

@misc{pith2026250602062,
  author       = {Pith},
  title        = {Pith review of: Predicting Blood Type: Assessing Model Performance with ROC Analysis},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ATHN4PWA}},
  note         = {Machine review of arXiv:2506.02062}
}
read the original abstract

Introduction: Personal identification is a critical aspect of forensic sciences, security, and healthcare. While conventional biometrics systems such as DNA profiling and iris scanning offer high accuracy, they are time-consuming and costly. Objectives: This study investigates the relationship between fingerprint patterns and ABO blood group classification to explore potential correlations between these two traits. Methods: The study analyzed 200 individuals, categorizing their fingerprints into three types: loops, whorls, and arches. Blood group classification was also recorded. Statistical analysis, including chi-square and Pearson correlation tests, was used to assess associations between fingerprint patterns and blood groups. Results: Loops were the most common fingerprint pattern, while blood group O+ was the most prevalent among the participants. Statistical analysis revealed no significant correlation between fingerprint patterns and blood groups (p > 0.05), suggesting that these traits are independent. Conclusions: Although the study showed limited correlation between fingerprint patterns and ABO blood groups, it highlights the importance of future research using larger and more diverse populations, incorporating machine learning approaches, and integrating multiple biometric signals. This study contributes to forensic science by emphasizing the need for rigorous protocols and comprehensive investigations in personal identification.

Figures

Figures reproduced from arXiv: 2506.02062 by the authors.

Figure 1
Figure 1. ROC Target class: A+ The [PITH_FULL_IMAGE:figures/full_fig_p008_1.png] view at source ↗
Figure 2
Figure 2. (ROC) Target class: A￾The [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗
Figure 3
Figure 3. (ROC) Target class: AB+ [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

78 extracted references · 48 canonical work pages

  1. [1]

    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). Available from: https://iajit.org/upload/files /A- Comparative-Study-on-Deep-Learning-and-Machine-Learning-Models-for-Human-Action- Recognition-in-Aeri...

    work page 2023

  2. [2]

    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

    work page 2023

  3. [3]

    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

    work page 2023

  4. [4]

    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

    work page 2024

  5. [5]

    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;1. Available from: https://dm.ageditor.ar/index.php/dm/article/view/419/782

    2024

  6. [6]

    Advanced Machine Learning Models for Real -Time Drone and Bird Differentiation in Aerial Surveillance Systems

    Alqaraleh M, Alzboon MS, Al-Batah M, Saleh O, Migdadi HS, Elrashidi A, et al. Advanced Machine Learning Models for Real -Time Drone and Bird Differentiation in Aerial Surveillance Systems. In: 2024 25th International Arab Conference on Information Technology (ACIT) [Internet]. IEEE; 2024. p. 1 –8. Available from: https://ieeexplore.ieee.org/document/10877066/

    arXiv 2024

  7. [7]

    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;2(74):74. Available from: https://latia.ageditor.uy/index.php/latia/article/view/74

    work page 2024

  8. [8]

    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

    work page 2024

Show all 78 references
  1. [9]

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

    Wahed MA, Alqaraleh M, Alzboon MS, Al -Batah MS. 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

  2. [10]

    AI-Driven UAV Distinction: Leveraging Advanced Machine Learning

    Alzboon MS, Alqaraleh M, Wahed MA, Alourani A, Bader AF, Al -Batah M. AI-Driven UAV Distinction: Leveraging Advanced Machine Learning. In: 2024 7th International Conference on Internet Applications, Protocols, and Services (NETAPPS) [Internet]. IEEE

    2024

  3. [11]

    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

  4. [12]

    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/in...

    2025

  5. [13]

    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

  6. [14]

    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

  7. [15]

    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 2...

    2024

  8. [16]

    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

  9. [17]

    Classifying Psychiatric Patients Using Machine Learning

    Alrjoob HM, Alazaidah R, Batyha R, Khafajeh H, Elsoud EA, Saeb Al -Sherideh A, et al. Classifying Psychiatric Patients Using Machine Learning. In: 2024 25th International Arab Conference on Information Technology (ACIT) [Internet]. IEEE; 2024. p. 1 –9. Available from: https://...

    2024

  10. [18]

    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

    2024

  11. [19]

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

    Subhi Al-Batah M, Alqaraleh M, Salem Alzboon M, Alourani A. Comparative performance of ensemble models in predicting dental provider types: insights from fee -for-service data. Data Metadata [Internet]. 2025 Mar 29;4:750. Available from: https://dm.ageditor.ar/index.php/dm/art...

    2025

  12. [20]

    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

  13. [21]

    Comprehensive Assessment of Cybersecurity Measures: Evaluating Incident Response, AI Integration, and Emerging Threats

    Wahed MA, Alzboon MS, Alqaraleh M, Halasa A, Al -Batah M, Bader AF. Comprehensive Assessment of Cybersecurity Measures: Evaluating Incident Response, AI Integration, and Emerging Threats. In: 2024 7th International Conference on Internet Applications, Protocols, and Services (...

    2024

  14. [22]

    Comprehensive Assessment of Cybersecurity Measures: Evaluating Incident Response, AI Integration, and Emerging Threats

    Wahed MA, Alzboon MS, Alqaraleh M, Halasa A, Al -Batah M, Bader AF. Comprehensive Assessment of Cybersecurity Measures: Evaluating Incident Response, AI Integration, and Emerging Threats. In: 2024 7th International Conference on Internet Applications, Protocols, and Services, ...

    2024

  15. [23]

    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

  16. [24]

    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

  17. [25]

    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

  18. [26]

    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

  19. [27]

    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

  20. [28]

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

    Alqaraleh M, Subhi Al-Batah M, Salem Alzboon M, Alzboon F, Alzboon L, Nayef Alamoush M. Echoes in the Genome: Smoking’s Epigenetic Fingerprints and Bidirectional Neurobiological Pathways in Addiction and Disease. Semin Med Writ Educ [Internet]. 2024 Dec 30;3. Available from: h...

    2024 doi

  21. [29]

    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

  22. [30]

    Enhancing Diagnostic Precision in Pediatric Urology: Machine Learning Models for Automated Grading of Vesicoureteral Reflux

    Wahed MA, Alzboon MS, Alqaraleh M, Al -Batah M, Bader AF, Wahed SA. Enhancing Diagnostic Precision in Pediatric Urology: Machine Learning Models for Automated Grading of Vesicoureteral Reflux. In: 2024 7th International Conference on Internet Applications, Protocols, and Servi...

    2024

  23. [31]

    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

  24. [32]

    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 2024. 2024. p. 1619–28

    2024

  25. [33]

    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;3:117. Available fro m: https://latia.ageditor...

    2025

  26. [34]

    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

  27. [35]

    From Complexity to Clarity: Improving Microarray Classification with Correlation-Based Feature Selection

    Alqaraleh M, Salem Alzboon M, Subhi Al-Batah M, Solayman Migdadi H. From Complexity to Clarity: 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

    2025

  28. [36]

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

    Alqaraleh M, Subhi Al-Batah M, Salem Alzboon M, Alzboon F, Alzboon L, Nayef Alamoush M. From Puffs to Predictions: Leveraging AI, Wearables, and Biomolecular Signatures to Decode Smoking’s Multidimensional Impact on Cardiovascular Health. Semin Med Wr it Educ [Internet]. 2024 ...

    2024 doi

  29. [37]

    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

  30. [38]

    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/gr20259 1

    2025 doi

  31. [39]

    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

  32. [40]

    Improving Oral Cancer Outcomes Through Machine Learning and Dimensionality Reduction

    Subhi Al -Batah M, Alqaraleh M, Salem Alzboon M. Improving Oral Cancer Outcomes Through Machine Learning and Dimensionality Reduction. Data Metadata [Internet]. 2025 Jan 2;3. Available from: https://dm.ageditor.ar/index.php/dm/article/view/570

    2025

  33. [41]

    Innovative Machine Learning Solutions for Automated Kidney Tumor Detection in CT Imaging Through Comparative Analysis

    Alqaraleh M, Alzboon MS, Al-Batah M, Migdadi HS, Saleh O, Alazaidah R, et al. Innovative Machine Learning Solutions for Automated Kidney Tumor Detection in CT Imaging Through Comparative Analysis. In: 2024 25th International Arab Conference on Information Technology (ACIT) [In...

    2024

  34. [42]

    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

  35. [43]

    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

  36. [44]

    Machine Learning Classification Algorithms for Accurate Breast Cancer Diagnosis

    Alzboon MS, Qawasmeh S, Alqaraleh M, Abuashour A, Bader AF, Al -Batah M. Machine Learning Classification Algorithms for Accurate Breast Cancer Diagnosis. In: 2023 3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA

    2023

  37. [45]

    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

  38. [46]

    Optimizing Genetic Algorithms with Multilayer Perceptron Networks for Enhancing TinyFace Recognition

    Subhi Al-Batah M, Alzboon M, Alqaraleh M. Optimizing Genetic Algorithms with Multilayer Perceptron Networks for Enhancing TinyFace Recognition. Data Metadata [Internet]. 2024 Dec 30;3. Available from: https://dm.ageditor.ar/index.php/dm/article/view/594

    2024

  39. [47]

    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;3:97. Available from: https://latia.ageditor.uy/index.php/latia/article/view/97

    2025

  40. [48]

    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

  41. [49]

    Prediction of Hypertension Disease Using Machine Learning Techniques: Case Study from Jordan

    Alazaidah R, Samara G, Katrawi A, Hadi W, Al -Safarini MY, Al -Mamoori F, et al. Prediction of Hypertension Disease Using Machine Learning Techniques: Case Study from Jordan. In: 2024 25th International Arab Conference on Information Technology (ACIT) [Internet]. IEEE; 2024. p...

    2024

  42. [50]

    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;3:81. Available from: http://dx.doi.org/10.56294/gr202581

    2025 doi

  43. [51]

    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

  44. [52]

    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 Tech...

    2023

  45. [53]

    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;3:63. Available from: https://gr.ageditor.ar/index.php/gr/article/view/63

    2025

  46. [54]

    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

  47. [55]

    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

  48. [56]

    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. 2024;15(11):1065–78

    2024

  49. [57]

    Superior Classification of Brain Cancer Types Through Machine Learning Techniques Applied to Magnetic Resonance Imaging

    Al-Batah M, Salem Alzboon M, Alqaraleh M. Superior Classification of Brain Cancer Types Through Machine Learning Techniques Applied to Magnetic Resonance Imaging. Data Metadata [Internet]. 2025 Jan 1;4:472. Available from: https://dm.ageditor.ar/index.php/dm/article/view/472

    2025

  50. [58]

    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

  51. [59]

    Technological Innovations in Autonomous Vehicles: A Focus on Sensor Fusion and Environmental Perception

    Abdel Wahed M, Al-Batah M, Salem Alzboon M, Fuad Bader A, Alqaraleh M. Technological Innovations in Autonomous Vehicles: A Focus on Sensor Fusion and Environmental Perception. 2024 7th International Conference on Internet Applications, Protocols, and Services, NETAPPS 2024. 2024

    2024

  52. [60]

    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 projec t Evaluation of Knowledg...

    2017

  53. [61]

    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

  54. [62]

    The Role of Perceived Trust in Embracing Artificial Intelligence Technologies: Insights from Jordan’s SME Sector

    Alzboon MS, Al-Shorman HM, Alka’awneh SMN, Saatchi SG, Alqaraleh MKS, Samara EIM, et al. The Role of Perceived Trust in Embracing Artificial Intelligence Technologies: Insights from Jordan’s SME Sector. In: Studies in Computational Intelligence [Inter net]

  55. [63]

    The Role of Perceived Trust in Embracing Artificial Intelligence Technologies: Insights from Jordan’s SME Sector

    Alzboon MS, Al-Shorman HM, Alka’awneh SMN, Saatchi SG, Alqaraleh MKS, Samara EIM, et al. The Role of Perceived Trust in Embracing Artificial Intelligence Technologies: Insights from Jordan’s SME Sector. In: Studies in Computational Intelligence [Inter net]. Springer Nature Swi...

    2024 doi

  56. [65]

    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

  57. [66]

    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

  58. [67]

    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

    2015

  59. [68]

    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

  60. [69]

    STUDY OF FINGERPRINT PATTERNS IN RELATIONSHIP WITH BLOOD GROUP AND GENDER IN SAURASHTRA REGION

    Kukadiya U, Trivedi P, Rathva A, Lakhani C. STUDY OF FINGERPRINT PATTERNS IN RELATIONSHIP WITH BLOOD GROUP AND GENDER IN SAURASHTRA REGION. Int J Anat Res [Internet]. 2020 Jun 5;8(2.3):7564 –7. Available from: https://www.ijmhr.org/IntJAnatRes/IJAR.2020.159

    2020

  61. [70]

    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

  62. [71]

    Distribution of ABO & Rh bloog group in relation to dermatoglyphics and BMI

    Sivagurunathan A, Subbulakshmi A, Veluramesh S, Anitha NJ. Distribution of ABO & Rh bloog group in relation to dermatoglyphics and BMI. null. 2020;144(April):266 –81

    2020

  63. [72]

    DR. Y. N. UMRANIYA DYNU, DR. H. H. MODI DHHM, DR. H. K. PRAJAPATI DHKP. Study of Correlation of Finger Print Patterns in Different ABO, Rh Blood Groups. Int J Sci Res [Internet]. 2012 Jun 1;2(9):337 –9. Available from: http://theglobaljournals.com/ijsr/file.php?val=September_2...

    2012

  64. [73]

    Prasad DM, . A. Blood Group Detection through Finger Print Images using Image Processing. Int J Res Appl Sci Eng Technol [Internet]. 2023 Jul 31;11(7):1350–4. Available from: https://www.ijraset.com/best -journal/blood-group-detection-through-finger- print-images-using-image-p...

    2023

  65. [74]

    Gender Specific Correlation between Lip Print, Fingerprint and Blood Groups among Adults aged 20-30 Years attending a Tertiary Health Care Centre

    Ghimire P, Ghimire S, Khanal A, Khapung A. Gender Specific Correlation between Lip Print, Fingerprint and Blood Groups among Adults aged 20-30 Years attending a Tertiary Health Care Centre. Nepal Med Coll J [Internet]. 2022 Sep 28;24(3):219 –26. Available from: https://www.nep...

    2022

  66. [75]

    Qualitative Analysis of Pattern of Finger Print in Relation to Gender and Blood Group

    Kushwaha V, Dev R, Verma S, Awasthi P, Pathak A, Yadav A, et al. Qualitative Analysis of Pattern of Finger Print in Relation to Gender and Blood Group. Indian Internet J Forensic Med Toxicol [Internet]. 2020;18(3and4):47 –9. Available from: https://acspublisher.com/journals/in...

    2020

  67. [76]

    Kavita V Horadi

    Swathi P, Sushmita K, Prof. Kavita V Horadi. Fingerprint Based Blood Group using Deep Learning. Int J Adv Res Sci Commun Technol [Internet]. 2024 Feb 8;699 –708. Available from: http://ijarsct.co.in/Paper15393.pdf

    2024

  68. [77]

    Correlation of lip print, finger print and blood groups in a Tamil Nadu based population

    Harsha L, Jayaraj G. Correlation of lip print, finger print and blood groups in a Tamil Nadu based population. J Pharm Sci Res. 2015;7(9):795–9

    2015

  69. [78]

    MAHALINGAM BHUVANESWARI, DR

    T MAHALAKSHMI, JINCY, PRAVEENA, DR. MAHALINGAM BHUVANESWARI, DR. SATHISH MUTHUKUMAR, DR. MERLIN JAYARAJ. Determination and Correlation of Finger Print Pattern and Blood Grouping in Diabetes Mellitus: An Analytical Study. Indian J Forensic Med Toxicol [Internet]. 2024 Apr 27;18...

    2024

  70. [80]

    Evaluation of Correlation between Blood Group System & Fingerprint Classification System in both female and male

    Patel PP, Christian N, Chauhan DJ, Ms, Varadiya A. Evaluation of Correlation between Blood Group System & Fingerprint Classification System in both female and male. null. 2021; FINANCING This work is supported by University of Tabuk, Zarqa University and Jadara University. CON...

    2021

Pith tools

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