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arxiv: 2412.14736 · v1 · submitted 2024-12-19 · 💻 cs.SE · cs.AI

Advances in Artificial Intelligence forDiabetes Prediction: Insights from a Systematic Literature Review

Pir Bakhsh Khokhar , Carmine Gravino , Fabio Palomba This is my paper

Pith reviewed 2026-05-23 07:33 UTC · model grok-4.3

classification 💻 cs.SE cs.AI
keywords machine learningdiabetes predictionsystematic reviewartificial intelligenceethical considerationshealthcare AIdatasets
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The pith

Machine learning can predict diabetes from standard datasets but requires ethical oversight and cross-field collaboration.

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

The paper performs a systematic literature review of machine learning methods applied to diabetes prediction. It surveys specific datasets including the Pima Indians Diabetes Database, algorithms such as CNN, SVM, logistic regression and XGBoost, training approaches, and common evaluation metrics. The authors synthesize performance results across these elements and draw the conclusion that technical progress alone is insufficient. A sympathetic reader cares because the review frames AI as a practical healthcare tool whose reliability depends on addressing non-technical factors that affect deployment and fairness.

Core claim

The systematic review finds that established machine learning algorithms achieve usable prediction performance on public diabetes datasets yet successful and responsible application depends on interdisciplinary collaboration and explicit attention to ethical considerations during model design and use.

What carries the argument

Systematic literature review that maps datasets, algorithms, and metrics while foregrounding ethical and collaborative requirements as necessary conditions for model utility.

If this is right

  • Prediction models built with XGBoost or SVM on datasets such as Pima Indians can reach practical accuracy levels.
  • Ethical guidelines must be incorporated into the training and evaluation pipeline for any diabetes prediction system.
  • Effective models are more likely to emerge when computer scientists work directly with clinicians and ethicists.
  • Choice of evaluation metrics should reflect real-world clinical impact rather than isolated accuracy scores alone.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same review structure could be applied to prediction tasks for other chronic conditions to surface recurring ethical patterns.
  • Regulators could draw on the identified datasets and algorithms to set minimum standards for transparency in healthcare AI.
  • Combining findings across multiple public datasets may improve model robustness beyond what single-dataset studies show.

Load-bearing premise

The papers and datasets chosen for the review form a representative and unbiased picture of current machine learning work on diabetes prediction.

What would settle it

A broader or updated search that locates many high-performing studies using different datasets or algorithms whose ethical handling differs substantially from those included in the review.

Figures

Figures reproduced from arXiv: 2412.14736 by Carmine Gravino, Fabio Palomba, Pir Bakhsh Khokhar.

Figure 1
Figure 1. Figure 1: PRISMA owchart of study identi cation, screening, and inclusion process. The information we obtained from the selected publica￾tions answered our research questions. This section offers a concise review of the most important findings that emerged from our investigation. 3.5. Quality assessment Before moving further with the process of extracting the material necessary to answer our research questions, we e… view at source ↗
Figure 2
Figure 2. Figure 2: Distribution of Publication by Year [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Top 5% Journals for Diabetes Prediction Research (2014-23) Journals play a vital role in disseminating the research done by people. In the context of diabetes prediction re￾search from 2014 to 2023, the top five journals contributing significantly to diabetes prediction research were Diabetes Care, IEEE Access, and IEEE Transactions on Biomedical Engineering. Scientific Reports and the Journal of Diabetes … view at source ↗
Figure 5
Figure 5. Figure 5: Longitudinal Studies: The Japanese study of Aizawa Hos￾pital from Matsumoto investigated 2,105 cases of adults with prediabetes follow-up data with an average observation period of 4. 7 years [79]. It also shows that the monitoring process is a vital aspect for one be able to notice the transi￾tion from prediabetes to diabetes. This way, the researchers would be able to determine potential early indicators… view at source ↗
Figure 4
Figure 4. Figure 4: Top 20 keywords used in the studies 4.1. RQ1 : On the Datasets and Their Characteristics When it comes to diabetes prediction research, the selec￾tion of datasets is rather crucial [37]. Data or datasets are the key input to the development of any predictive model and the quality of the data defines the efficacy of the resulting model. Advanced datasets include people of different demography, diseases, and… view at source ↗
Figure 6
Figure 6. Figure 6: , we are provided with a rather obvious realization that age variable is used as the independent variable in the vast majority of the studies to a significant extent. Looking at the process of forecasting diabetes, there are some factors that include but are not limited to the body mass index, blood pressure, glucose level, cholesterol level, insulin level, family history of diabetes, physical activity and… view at source ↗
Figure 7
Figure 7. Figure 7: Percentage of ML Algorithms used for Diabetes Prediction Learning that utilizes more models to enhance performance makes up 6% of approaches. Evolutionary Computing is less than 2% and Bayesian Inference also less than 2% indicating that several methods are used for enhancing the accuracy and complexity of diabetes prediction [PITH_FULL_IMAGE:figures/full_fig_p015_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Percentage of Studies Using Speci c Evaluation Metrics models for diabetes. The discussion covers the choice of datasets and their quality, the chosen machine learning algo￾rithms and training paradigms, and the evaluation scenarios and measures used in the assessment of the performance of the model. In this review, the strengths and limitations of the different approaches are discussed based on many publi… view at source ↗
read the original abstract

This systematic review explores the use of machine learning (ML) in predicting diabetes, focusing on datasets, algorithms, training methods, and evaluation metrics. It examines datasets like the Singapore National Diabetic Retinopathy Screening program, REPLACE-BG, National Health and Nutrition Examination Survey, and Pima Indians Diabetes Database. The review assesses the performance of ML algorithms like CNN, SVM, Logistic Regression, and XGBoost in predicting diabetes outcomes. The study emphasizes the importance of interdisciplinary collaboration and ethical considerations in ML-based diabetes prediction models.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The paper conducts a systematic literature review of machine learning approaches for diabetes prediction. It surveys datasets (e.g., Pima Indians Diabetes Database, NHANES, REPLACE-BG) and algorithms (CNN, SVM, logistic regression, XGBoost), reports on their performance, and concludes that interdisciplinary collaboration and ethical considerations are essential for future work in the area.

Significance. A properly executed systematic review could usefully map the current state of ML-based diabetes prediction and draw attention to ethical and collaborative requirements. The present manuscript, however, supplies no evidence that its synthesis rests on a reproducible or unbiased sample of the literature, which substantially reduces its potential contribution.

major comments (2)
  1. [Abstract] Abstract: the manuscript is presented as a systematic literature review yet reports neither the databases searched, search strings, date ranges, inclusion/exclusion criteria, nor a PRISMA flow diagram. Without these elements the claim that the reviewed literature supports specific conclusions about algorithm performance or the necessity of ethical considerations cannot be evaluated for selection bias.
  2. [Abstract] Abstract: performance assessments of CNN, SVM, logistic regression, and XGBoost are asserted without any tabulated quantitative results, cross-study comparisons, or reference to the number of primary studies that contributed each metric. This leaves the central descriptive claims about relative algorithm effectiveness unsupported.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our manuscript. We agree that the presentation of the systematic literature review requires greater methodological transparency and more explicit quantitative support for claims about algorithm performance. We will revise the manuscript to address these points.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the manuscript is presented as a systematic literature review yet reports neither the databases searched, search strings, date ranges, inclusion/exclusion criteria, nor a PRISMA flow diagram. Without these elements the claim that the reviewed literature supports specific conclusions about algorithm performance or the necessity of ethical considerations cannot be evaluated for selection bias.

    Authors: We acknowledge that the current manuscript does not report the search strategy, databases, search strings, date ranges, inclusion/exclusion criteria, or include a PRISMA flow diagram. This was an omission in reporting. In the revised version we will add a dedicated Methods section that specifies the databases searched, the exact search strings, the date range covered, the inclusion and exclusion criteria applied, and a PRISMA flow diagram documenting the study selection process. These additions will allow readers to assess reproducibility and potential selection bias. revision: yes

  2. Referee: [Abstract] Abstract: performance assessments of CNN, SVM, logistic regression, and XGBoost are asserted without any tabulated quantitative results, cross-study comparisons, or reference to the number of primary studies that contributed each metric. This leaves the central descriptive claims about relative algorithm effectiveness unsupported.

    Authors: The manuscript summarizes performance findings from the reviewed studies, but we agree that the absence of tabulated quantitative results and explicit cross-study comparisons weakens the support for claims about relative effectiveness. In the revision we will add a results table that compiles key performance metrics (accuracy, sensitivity, specificity, AUC) reported across the primary studies for each algorithm, together with the number of studies contributing each metric and brief cross-study comparisons where the data permit. revision: yes

Circularity Check

0 steps flagged

No circularity: descriptive review of external literature only

full rationale

The paper is a systematic literature review summarizing datasets, algorithms, and findings from external studies on ML-based diabetes prediction. It contains no original derivations, equations, fitted parameters, predictions, or ansatzes that could reduce to its own inputs. The emphasis on interdisciplinary collaboration and ethics is framed as an insight drawn from the reviewed papers rather than a self-derived result. No self-citation chains, uniqueness theorems, or renamings of known results are present. The review is self-contained against external benchmarks by design, with any methodological limitations (e.g., search strategy) falling under correctness rather than circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

As a literature review the paper introduces no free parameters, no new axioms beyond standard review practices, and no invented entities; it relies entirely on cited prior work.

axioms (1)
  • domain assumption Standard systematic review methodology is used to identify and synthesize relevant papers.
    The paper is explicitly described as a systematic literature review.

pith-pipeline@v0.9.0 · 5612 in / 1102 out tokens · 40152 ms · 2026-05-23T07:33:04.191529+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

86 extracted references · 86 canonical work pages

  1. [1]

    Type 2 diabetes

    Ahmad, E., Lim, S., Lamptey, R., Webb, D.R., Davies, M.J., 2022. Type 2 diabetes. The Lancet 400, 1803–1820

    work page 2022

  2. [2]

    Deep learningmodellingtechniques:currentprogress,applications,advan- tages, and challenges

    Ahmed, S.F., Alam, M.S.B., Hassan, M., Rozbu, M.R., Ishtiak, T., Rafa, N., Mofijur, M., Shawkat Ali, A., Gandomi, A.H., 2023. Deep learningmodellingtechniques:currentprogress,applications,advan- tages, and challenges. Artificial Intelligence Review 56, 13521– 13617

    work page 2023

  3. [3]

    Hand movement-based diabetes detection using machine learning tech- niques

    Al-Tarawneh, M., Muheilan, M., Al Tarawneh, Z., 2021. Hand movement-based diabetes detection using machine learning tech- niques. International Journal on Engineering Applications (IREA) 9, 234–242

    work page 2021

  4. [4]

    Machine learning methods for diabetesprevalenceclassificationinsaudiarabia.Modelling4,37–55

    Almutairi, E.S., Abbod, M.F., 2023. Machine learning methods for diabetesprevalenceclassificationinsaudiarabia.Modelling4,37–55

    work page 2023

  5. [5]

    The role of physical therapists in fighting the type 2 diabetes epidemic patients attending primary healthcare centers in makkah city, saudi arabia in 2022

    Alsaigh, A., Almalki, F.A.M., Alnefaie, M.A.M., et al., 2022. The role of physical therapists in fighting the type 2 diabetes epidemic patients attending primary healthcare centers in makkah city, saudi arabia in 2022. Annals of the Romanian Society for Cell Biology 26, 3135–3148

    work page 2022

  6. [6]

    Reverse engineering and evaluation of prediction models for progression to type 2 diabetes: an application of machine learning using electronic health records

    Anderson, J.P., Parikh, J.R., Shenfeld, D.K., Ivanov, V., Marks, C., Church, B.W., Laramie, J.M., Mardekian, J., Piper, B.A., Willke, R.J., et al., 2016. Reverse engineering and evaluation of prediction models for progression to type 2 diabetes: an application of machine learning using electronic health records. Journal of diabetes science and technology 10, 6–18

    work page 2016

  7. [7]

    Anggraeni, Z., Wibawa, H.A., 2019. Detection of the emergence of exudate on the image of retina using extreme learning machine method, in: 2019 3rd International Conference on Informatics and Computational Sciences (ICICoS), IEEE. pp. 1–6

    work page 2019

  8. [8]

    Deep learning predicts oct measures of diabetic macular thickening from color fundus photographs

    Arcadu, F., Benmansour, F., Maunz, A., Michon, J., Haskova, Z., McClintock,D.,Adamis,A.P.,Willis,J.R.,Prunotto,M.,2019. Deep learning predicts oct measures of diabetic macular thickening from color fundus photographs. Investigative ophthalmology & visual science 60, 852–857

    work page 2019

  9. [9]

    Physical activity/exercise and diabetes mellitus

    Association, A.D., 2003. Physical activity/exercise and diabetes mellitus. Diabetes care 26, s73–s77

    work page 2003

  10. [10]

    A novel machine learning framework for diagnosing the type 2 diabetics using temporal fuzzy ant miner decision tree classifier with temporal weighted genetic algorithm

    Bhuvaneswari, G., Manikandan, G., 2018. A novel machine learning framework for diagnosing the type 2 diabetics using temporal fuzzy ant miner decision tree classifier with temporal weighted genetic algorithm. Computing 100, 759–772

    work page 2018

  11. [11]

    A systematic literaturereviewondiabeticretinopathyusinganartificialintelligence approach

    Bidwai, P., Gite, S., Pahuja, K., Kotecha, K., 2022. A systematic literaturereviewondiabeticretinopathyusinganartificialintelligence approach. Big Data and Cognitive Computing 6, 152

    work page 2022

  12. [12]

    Program on the global demography of aging

    Bloom, D.E., Canning, D., Fink, G., 2009. Program on the global demography of aging. Harvard University. Oct

    work page 2009

  13. [13]

    Buccheri,E.,Dell’Aquila,D.,Russo,M.,2021. Artificialintelligence in health data analysis: the darwinian evolution theory suggests an extremely simple and zero-cost large-scale screening tool for predi- abetes and type 2 diabetes. Diabetes Research and Clinical Practice 174, 108722

    work page 2021

  14. [14]

    Machinelearningbaseddiabetesclassificationandpre- dictionforhealthcareapplications

    Butt,U.M.,Letchmunan,S.,Ali,M.,Hassan,F.H.,Baqir,A.,Sherazi, H.H.R.,2021. Machinelearningbaseddiabetesclassificationandpre- dictionforhealthcareapplications. Journalofhealthcareengineering 2021, 9930985

    work page 2021

  15. [15]

    The steps to doing a systems literature review (slr)

    Cabrera, D., Cabrera, L.L., 2023. The steps to doing a systems literature review (slr). Journal of Systems Thinking Preprints

    work page 2023

  16. [16]

    Prediction of incident diabetes in the jackson heart study using high-dimensional machine learning

    Casanova,R.,Saldana,S.,Simpson,S.L.,Lacy,M.E.,Subauste,A.R., Blackshear, C., Wagenknecht, L., Bertoni, A.G., 2016. Prediction of incident diabetes in the jackson heart study using high-dimensional machine learning. PloS one 11, e0163942

    work page 2016

  17. [17]

    Type 2 diabetes

    Chatterjee, S., Khunti, K., Davies, M.J., 2017. Type 2 diabetes. The lancet 389, 2239–2251

    work page 2017

  18. [18]

    The diagnostics of diabetes mellitus based on ensemble modeling and hair/urine element level analysis

    Chen, H., Tan, C., Lin, Z., Wu, T., 2014. The diagnostics of diabetes mellitus based on ensemble modeling and hair/urine element level analysis. Computers in biology and medicine 50, 70–75

    work page 2014

  19. [19]

    Chen, W., Chen, S., Zhang, H., Wu, T., 2017. A hybrid prediction model for type 2 diabetes using k-means and decision tree, in: 2017 8th IEEE international conference on software engineering and ser- vice science (ICSESS), IEEE. pp. 386–390

    work page 2017

  20. [20]

    Chetoui,M.,Akhloufi,M.A.,Kardouchi,M.,2018.Diabeticretinopa- thy detection using machine learning and texture features, in: 2018 IEEE Canadian conference on electrical & computer engineering (CCECE), IEEE. pp. 1–4

    work page 2018

  21. [21]

    The benefits of the matthews correlation coefficient (mcc) over the diagnostic odds ratio (dor) in binary classification assessment

    Chicco, D., Starovoitov, V., Jurman, G., 2021. The benefits of the matthews correlation coefficient (mcc) over the diagnostic odds ratio (dor) in binary classification assessment. Ieee Access 9, 47112– 47124

    work page 2021

  22. [22]

    Screening for prediabetes using machinelearningmodels

    Choi, S.B., Kim, W.J., Yoo, T.K., Park, J.S., Chung, J.W., Lee, Y.h., Kang, E.S., Kim, D.W., 2014. Screening for prediabetes using machinelearningmodels. Computationalandmathematicalmethods in medicine 2014, 618976

    work page 2014

  23. [23]

    An effective approach for detecting diabetes using deep learning techniques based on convo- lutional lstm networks

    Chowdary, P.B.K., Kumar, R.U., 2021. An effective approach for detecting diabetes using deep learning techniques based on convo- lutional lstm networks. International Journal of Advanced Computer Science and Applications 12, 519–525

    work page 2021

  24. [24]

    Artificial intelligence for diabetes management and decision support: literature review

    Contreras, I., Vehi, J., 2018. Artificial intelligence for diabetes management and decision support: literature review. Journal of medical Internet research 20, e10775

    work page 2018

  25. [25]

    The type 2 diabetes knowledge portal: An open access genetic resource dedicated to type 2 diabetes and related traits

    Costanzo, M.C., von Grotthuss, M., Massung, J., Jang, D., Caulkins, L., Koesterer, R., Gilbert, C., Welch, R.P., Kudtarkar, P., Hoang, Q., et al., 2023. The type 2 diabetes knowledge portal: An open access genetic resource dedicated to type 2 diabetes and related traits. Cell metabolism 35, 695–710

    work page 2023

  26. [26]

    Type 1 diabetes

    Daneman, D., 2006. Type 1 diabetes. The Lancet 367, 847–858

    work page 2006

  27. [27]

    Long-term relapse of type 2 diabetes after roux-en-y gastric bypass: prediction and clinical relevance

    Debédat, J., Sokolovska, N., Coupaye, M., Panunzi, S., Chakaroun, R.,Genser,L.,deTurenne,G.,Bouillot,J.L.,Poitou,C.,Oppert,J.M., et al., 2018. Long-term relapse of type 2 diabetes after roux-en-y gastric bypass: prediction and clinical relevance. Diabetes Care 41, 2086–2095

    work page 2018

  28. [28]

    Predictionoftype2diabetesbasedon machine learning algorithm

    Deberneh,H.M.,Kim,I.,2021. Predictionoftype2diabetesbasedon machine learning algorithm. International journal of environmental research and public health 18, 3317

    work page 2021

  29. [29]

    DeFronzo, R.A., Ferrannini, E., Groop, L., Henry, R.R., Herman, W.H., Holst, J.J., Hu, F.B., Kahn, C.R., Raz, I., Shulman, G.I., et al.,

    work page

  30. [30]

    Nature reviews Disease primers 1, 1–22

    Type 2 diabetes mellitus. Nature reviews Disease primers 1, 1–22

    work page

  31. [31]

    A data- driven approach to predicting diabetes and cardiovascular disease with machine learning

    Dinh, A., Miertschin, S., Young, A., Mohanty, S.D., 2019. A data- driven approach to predicting diabetes and cardiovascular disease with machine learning. BMC medical informatics and decision making 19, 1–15

    work page 2019

  32. [32]

    Ehrenstein, V., Kharrazi, H., Lehmann, H., Taylor, C.O., 2019. Ob- taining data from electronic health records, in: Tools and technolo- gies for registry interoperability, registries for evaluating patient out- comes: A user’s guide, 3rd edition, Addendum 2 [Internet]. Agency for Healthcare Research and Quality (US)

    work page 2019

  33. [33]

    Machinelearningtoolsforlong-termtype2diabetes risk prediction

    Fazakis,N.,Kocsis,O.,Dritsas,E.,Alexiou,S.,Fakotakis,N.,Mous- takas,K.,2021. Machinelearningtoolsforlong-termtype2diabetes risk prediction. ieee Access 9, 103737–103757

    work page 2021

  34. [34]

    Data- based algorithms and models using diabetics real data for blood glucoseandhypoglycaemiaprediction–asystematicliteraturereview

    Felizardo, V., Garcia, N.M., Pombo, N., Megdiche, I., 2021. Data- based algorithms and models using diabetics real data for blood glucoseandhypoglycaemiaprediction–asystematicliteraturereview. Artificial Intelligence in Medicine 118, 102120

    work page 2021

  35. [35]

    Machine learning and deep learning predictive models for type 2 diabetes: a systematic review

    Fregoso-Aparicio, L., Noguez, J., Montesinos, L., García-García, J.A., 2021. Machine learning and deep learning predictive models for type 2 diabetes: a systematic review. Diabetology & metabolic syndrome 13, 148

    work page 2021

  36. [36]

    Early detection of diabetic retinopathy using pca-firefly based deep learning model

    Gadekallu, T.R., Khare, N., Bhattacharya, S., Singh, S., Maddikunta, P.K.R., Ra, I.H., Alazab, M., 2020. Early detection of diabetic retinopathy using pca-firefly based deep learning model. Electronics 9, 274

    work page 2020

  37. [37]

    Automated identification of diabetic retinopathy using deep learning

    Gargeya, R., Leng, T., 2017. Automated identification of diabetic retinopathy using deep learning. Ophthalmology 124, 962–969

    work page 2017

  38. [38]

    Learning and data selection in big datasets, in: International Confer- ence on Machine Learning, PMLR

    Ghadikolaei, H.S., Ghauch, H., Fischione, C., Skoglund, M., 2019. Learning and data selection in big datasets, in: International Confer- ence on Machine Learning, PMLR. pp. 2191–2200

    work page 2019

  39. [39]

    Correlation-based feature selection for machine learning

    Hall, M.A., 1999. Correlation-based feature selection for machine learning. Ph.D. thesis. The University of Waikato. Pir Bakhsh et al.: Preprint submitted to Elsevier Page 23 of 25 Advances in Arti cial Intelligence for Diabetes Prediction: Insights from a Systematic Literature Review

    work page 1999

  40. [40]

    Identifying continuousglucosemonitoringdatausingmachinelearning.Diabetes Technology & Therapeutics 24, 403–408

    Herrero, P., Reddy, M., Georgiou, P., Oliver, N.S., 2022. Identifying continuousglucosemonitoringdatausingmachinelearning.Diabetes Technology & Therapeutics 24, 403–408

    work page 2022

  41. [41]

    Systematicmapandreview of predictive techniques in diabetes self-management

    Idrissi,T.E.,Idri,A.,Bakkoury,Z.,2019. Systematicmapandreview of predictive techniques in diabetes self-management. International Journal of Information Management 46, 263–277

    work page 2019

  42. [42]

    Anexpertsystemfordiabetespredictionusingautotunedmulti-layer perceptron,in:2017Intelligentsystemsconference(IntelliSys),IEEE

    Jahangir, M., Afzal, H., Ahmed, M., Khurshid, K., Nawaz, R., 2017. Anexpertsystemfordiabetespredictionusingautotunedmulti-layer perceptron,in:2017Intelligentsystemsconference(IntelliSys),IEEE. pp. 722–728

    work page 2017

  43. [43]

    Distinguising proof of dia- betic retinopathy detection by hybrid approaches in two dimensional retinal fundus images

    Karkuzhali, S., Manimegalai, D., 2019. Distinguising proof of dia- betic retinopathy detection by hybrid approaches in two dimensional retinal fundus images. Journal of medical systems 43, 1–12

    work page 2019

  44. [44]

    Detectionofmulti-classretinaldiseasesusing artificial intelligence: an expeditious learning using deep cnn with minimal data

    Karthikeyan, S., Sanjay, K.P., Madhusudan, R., Sundaramoorthy, S., Namboori,P.K.,2019. Detectionofmulti-classretinaldiseasesusing artificial intelligence: an expeditious learning using deep cnn with minimal data. Biomedical & Pharmacology Journal 12, 1577

    work page 2019

  45. [45]

    Journal of ICT Standardization 10, 179–199

    Khan, M.Z., Mangayarkarasi, R., Vanmathi, C., Angulakshmi, M., 2022.Bio-inspiredpsoforimprovingneuralbaseddiabetesprediction system. Journal of ICT Standardization 10, 179–199

    work page 2022

  46. [46]

    Genetic prediction of type2diabetesusingdeepneuralnetwork

    Kim, J., Kim, J., Kwak, M., Bajaj, M., 2018. Genetic prediction of type2diabetesusingdeepneuralnetwork. Clinicalgenetics93,822– 829

    work page 2018

  47. [47]

    A systematic comparison of feature space effects on disease classifier performance for phenotype identi- fication of five diseases

    Kotfila, C., Uzuner, Ö., 2015. A systematic comparison of feature space effects on disease classifier performance for phenotype identi- fication of five diseases. Journal of biomedical informatics 58, S92– S102

    work page 2015

  48. [48]

    Predictive models for diabetes mellitus using machine learning tech- niques

    Lai, H., Huang, H., Keshavjee, K., Guergachi, A., Gao, X., 2019. Predictive models for diabetes mellitus using machine learning tech- niques. BMC endocrine disorders 19, 1–9

    work page 2019

  49. [49]

    Larabi-Marie-Sainte, S., Aburahmah, L., Almohaini, R., Saba, T.,

    work page

  50. [50]

    Applied Sciences 9, 4604

    Current techniques for diabetes prediction: review and case study. Applied Sciences 9, 4604

    work page

  51. [51]

    Lee,A.Y.,Yanagihara,R.T.,Lee,C.S.,Blazes,M.,Jung,H.C.,Chee, Y.E., Gencarella, M.D., Gee, H., Maa, A.Y., Cockerham, G.C., et al.,

    work page

  52. [52]

    Diabetes care 44, 1168–1175

    Multicenter, head-to-head, real-world validation study of seven automated artificial intelligence diabetic retinopathy screening systems. Diabetes care 44, 1168–1175

    work page

  53. [53]

    Performance assessment of different machine learning approaches in predicting diabetic ketoaci- dosis in adults with type 1 diabetes using electronic health records data

    Li, L., Lee, C.C., Zhou, F.L., Molony, C., Doder, Z., Zalmover, E., Sharma, K., Juhaeri, J., Wu, C., 2021. Performance assessment of different machine learning approaches in predicting diabetic ketoaci- dosis in adults with type 1 diabetes using electronic health records data. Pharmacoepidemiology and drug safety 30, 610–618

    work page 2021

  54. [54]

    Machine learning models for blood glucose level prediction in patients with diabetes mellitus: Systematic review and network meta-analysis

    Liu,K.,Li,L.,Ma,Y.,Jiang,J.,Liu,Z.,Ye,Z.,Liu,S.,Pu,C.,Chen, C., Wan, Y., et al., 2023. Machine learning models for blood glucose level prediction in patients with diabetes mellitus: Systematic review and network meta-analysis. JMIR Medical Informatics 11, e47833

    work page 2023

  55. [55]

    Diabetic retinopathy screening using artificial intelligence and handheld smartphone-based retinal camera

    Malerbi, F.K., Andrade, R.E., Morales, P.H., Stuchi, J.A., Lencione, D., de Paulo, J.V., Carvalho, M.P., Nunes, F.S., Rocha, R.M., Ferraz, D.A., et al., 2022. Diabetic retinopathy screening using artificial intelligence and handheld smartphone-based retinal camera. Journal of diabetes science and technology 16, 716–723

    work page 2022

  56. [56]

    Type 2 diabetes screening test by means of a pulse oximeter

    Moreno, E.M., Lujan, M.J.A., Rusinol, M.T., Fernandez, P.J., Man- rique,P.N.,Trivino,C.A.,Miquel,M.P.,Rodriguez,M.A.,Burguillos, M.J.G., 2016. Type 2 diabetes screening test by means of a pulse oximeter. IEEE Transactions on Biomedical Engineering 64, 341– 351

    work page 2016

  57. [57]

    A review of evaluation metrics in machine learning algorithms, in: Computer Science On- line Conference, Springer

    Naidu, G., Zuva, T., Sibanda, E.M., 2023. A review of evaluation metrics in machine learning algorithms, in: Computer Science On- line Conference, Springer. pp. 15–25

    work page 2023

  58. [58]

    Predicting the onset of type 2 diabetesusingwideanddeeplearningwithelectronichealthrecords

    Nguyen, B.P., Pham, H.N., Tran, H., Nghiem, N., Nguyen, Q.H., Do, T.T., Tran, C.T., Simpson, C.R., 2019. Predicting the onset of type 2 diabetesusingwideanddeeplearningwithelectronichealthrecords. Computer methods and programs in biomedicine 182, 105055

    work page 2019

  59. [59]

    Nijalingappa, P., Sandeep, B., 2015. Machine learning approach for the identification of diabetes retinopathy and its stages, in: 2015 International conference on applied and theoretical computing and communication technology (iCATccT), IEEE. pp. 653–658

    work page 2015

  60. [60]

    Average weighted objectivedistance-basedmethodfortype2diabetesprediction

    Nuankaew, P., Chaising, S., Temdee, P., 2021. Average weighted objectivedistance-basedmethodfortype2diabetesprediction. IEEE Access 9, 137015–137028

    work page 2021

  61. [61]

    Comparisonofmachine-learning algorithms to build a predictive model for detecting undiagnosed diabetes-elsa-brasil: accuracy study

    Olivera, A.R., Roesler, V., Iochpe, C., Schmidt, M.I., Vigo, Á., Barreto,S.M.,Duncan,B.B.,2017. Comparisonofmachine-learning algorithms to build a predictive model for detecting undiagnosed diabetes-elsa-brasil: accuracy study. Sao Paulo Medical Journal 135, 234–246

    work page 2017

  62. [62]

    The prisma 2020 statement: an updated guideline for reporting systematic reviews

    Page, M.J., McKenzie, J.E., Bossuyt, P.M., Boutron, I., Hoffmann, T.C.,Mulrow,C.D.,Shamseer,L.,Tetzlaff,J.M.,Akl,E.A.,Brennan, S.E., et al., 2021. The prisma 2020 statement: an updated guideline for reporting systematic reviews. bmj 372

    work page 2021

  63. [63]

    Early metabolic markersidentifypotentialtargetsforthepreventionoftype2diabetes

    Peddinti, G., Cobb, J., Yengo, L., Froguel, P., Kravić, J., Balkau, B., Tuomi, T., Aittokallio, T., Groop, L., 2017. Early metabolic markersidentifypotentialtargetsforthepreventionoftype2diabetes. Diabetologia 60, 1740–1750

    work page 2017

  64. [64]

    Ieee Access 8, 219308–219321

    Pustozerov, E.A., Tkachuk, A.S., Vasukova, E.A., Anopova, A.D., Kokina, M.A., Gorelova, I.V., Pervunina, T.M., Grineva, E.N., Popova,P.V.,2020.Machinelearningapproachforpostprandialblood glucose prediction in gestational diabetes mellitus. Ieee Access 8, 219308–219321

    work page 2020

  65. [65]

    Machine learning models for data- driven prediction of diabetes by lifestyle type

    Qin, Y., Wu, J., Xiao, W., Wang, K., Huang, A., Liu, B., Yu, J., Li, C., Yu, F., Ren, Z., 2022. Machine learning models for data- driven prediction of diabetes by lifestyle type. International journal of environmental research and public health 19, 15027

    work page 2022

  66. [66]

    Electronichealthrecord drivenpredictionforgestationaldiabetesmellitusinearlypregnancy

    Qiu, H., Yu, H.Y., Wang, L.Y., Yao, Q., Wu, S.N., Yin, C., Fu, B., Zhu,X.J.,Zhang,Y.L.,Xing,Y.,etal.,2017. Electronichealthrecord drivenpredictionforgestationaldiabetesmellitusinearlypregnancy. Scientific reports 7, 16417

    work page 2017

  67. [67]

    The impact of oversampling with smote on the performanceof3classifiers inpredictionoftype2diabetes

    Ramezankhani, A., Pournik, O., Shahrabi, J., Azizi, F., Hadaegh, F., Khalili, D., 2016. The impact of oversampling with smote on the performanceof3classifiers inpredictionoftype2diabetes. Medical decision making 36, 137–144

    work page 2016

  68. [68]

    Analysisofcomputationaltechniques fordiabetesdiagnosisusingthecombinationofiris-basedfeaturesand physiological parameters

    Samant,P.,Agarwal,R.,2019. Analysisofcomputationaltechniques fordiabetesdiagnosisusingthecombinationofiris-basedfeaturesand physiological parameters. Neural Computing and Applications 31, 8441–8453

    work page 2019

  69. [69]

    Sangi, M., Win, K.T., Shirvani, F., Namazi-Rad, M.R., Shukla, N.,

    work page

  70. [70]

    Applying a novel combination of techniques to develop a predictivemodelfordiabetescomplications.PLoSOne10,e0121569

    work page

  71. [71]

    [retracted] a comprehensive review of various diabetic prediction models: A literature survey

    Saxena,R.,Sharma,S.K.,Gupta,M.,Sampada,G.,2022. [retracted] a comprehensive review of various diabetic prediction models: A literature survey. Journal of Healthcare Engineering 2022, 8100697

    work page 2022

  72. [72]

    Dia- betes as a risk factor for poor early outcomes in patients hospitalized with covid-19

    Seiglie, J., Platt, J., Cromer, S.J., Bunda, B., Foulkes, A.S., Bassett, I.V., Hsu, J., Meigs, J.B., Leong, A., Putman, M.S., et al., 2020. Dia- betes as a risk factor for poor early outcomes in patients hospitalized with covid-19. Diabetes care 43, 2938–2944

    work page 2020

  73. [73]

    Analysis of diabetes mellitus for early prediction using optimal features selection

    Sneha, N., Gangil, T., 2019. Analysis of diabetes mellitus for early prediction using optimal features selection. Journal of Big data 6, 1–19

    work page 2019

  74. [74]

    Automated detection of diabetes using cnn and cnn-lstm network and heart rate signals

    Swapna, G., Kp, S., Vinayakumar, R., 2018a. Automated detection of diabetes using cnn and cnn-lstm network and heart rate signals. Procedia computer science 132, 1253–1262

    work page

  75. [75]

    Diabetesdetection using deep learning algorithms

    Swapna,G.,Vinayakumar,R.,Soman,K.,2018b. Diabetesdetection using deep learning algorithms. ICT express 4, 243–246

    work page

  76. [76]

    Detection of diabetic retinopathy from ultra-widefield scanning laser ophthalmoscope images: a multicenter deep learning analysis

    Tang, F., Luenam, P., Ran, A.R., Quadeer, A.A., Raman, R., Sen, P., Khan, R., Giridhar, A., Haridas, S., Iglicki, M., et al., 2021. Detection of diabetic retinopathy from ultra-widefield scanning laser ophthalmoscope images: a multicenter deep learning analysis. Oph- thalmology Retina 5, 1097–1106

    work page 2021

  77. [77]

    Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes

    Ting, D.S.W., Cheung, C.Y.L., Lim, G., Tan, G.S.W., Quang, N.D., Gan, A., Hamzah, H., Garcia-Franco, R., San Yeo, I.Y., Lee, S.Y., et al., 2017. Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. Jama 318, 2211–2223

    work page 2017

  78. [78]

    Usman, T.M., Saheed, Y.K., Nsang, A., Ajibesin, A., Rakshit, S.,

    work page

  79. [79]

    Artificial intelligence in medicine 143, 102617

    A systematic literature review of machine learning based risk prediction models for diabetic retinopathy progression. Artificial intelligence in medicine 143, 102617. Pir Bakhsh et al.: Preprint submitted to Elsevier Page 24 of 25 Advances in Arti cial Intelligence for Diabetes Prediction: Insights from a Systematic Literature Review

    work page

  80. [80]

    Predicting youth diabetes risk using nhanes data and machine learn- ing

    Vangeepuram, N., Liu, B., Chiu, P.h., Wang, L., Pandey, G., 2021. Predicting youth diabetes risk using nhanes data and machine learn- ing. Scientific reports 11, 11212

    work page 2021

Showing first 80 references.