Diabetes Prediction and Management Using Machine Learning Approaches
DOI:
https://doi.org/10.56294/dm2025545Keywords:
Diabetes, Decision trees, Machine Learning, Diagnosis, Support Vector MachineAbstract
Diabetes has emerged as a significant global health issue, especially with the increasing number of cases in many countries. This trend Underlines the need for a greater emphasis on early detection and proactive management to avert or mitigate the severe health complications of this disease. Over recent years, machine learning algorithms have shown promising potential in predicting diabetes risk and are beneficial for practitioners. Objective: This study highlights the prediction capabilities of statistical and non-statistical machine learning methods over Diabetes risk classification in 768 samples from the Pima Indians Diabetes Database. It consists of the significant demographic and clinical features of age, body mass index (BMI) and blood glucose levels that greatly depend on the vulnerability against Diabetes. The experimentation assesses the various types of machine learning algorithms in terms of accuracy and effectiveness regarding diabetes prediction. These algorithms include Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors, Naive Bayes, Support Vector Machine, Gradient Boosting and Neural Network Models. The results show that the Neural Network algorithm gained the highest predictive accuracy with 78.57%, and then the Random Forest algorithm had the second position with 76.30% accuracy. These findings show that machine learning techniques are not just highly effective. Still, they also can potentially act as early screening tools in predicting Diabetes within a data-driven fashion with valuable information on who is more likely to get affected. In addition, this study can help to realize the potential of machine learning for timely intervention over the longer term, which is a step towards reducing health outcomes and disease burden attributable to Diabetes on healthcare systems.
References
1. 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.
2. 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.
3. 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.
4. 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;3. Available from: https://dm.ageditor.ar/index.php/dm/article/view/419/782
5. Alqaraleh M, Abdel M. Advancing Medical Image Analysis : The Role of Adaptive Optimization Techniques in Enhancing COVID-19 Detection , Lung Infection , and Tumor Segmentation Avances en el análisis de imágenes médicas : el papel de las técnicas de optimización adaptativa para. LatIA. 2024;2(74).
6. Alzboon MS, Alqaraleh M, Al-Batah MS. AI in the Sky: Developing Real-Time UAV Recognition Systems to Enhance Military Security. Data Metadata. 2024;3(417).
7. 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.
8. Muhyeeddin Alqaraleh, Mohammad Al-Batah, Mowafaq Salem Alzboon EA. Automated quantification of vesicoureteral reflux using machine learning with advancing diagnostic precision. Data Metadata. 2025;4:460.
9. Mohammad Al-Batah, Mowafaq Salem Alzboon, Muhyeeddin Alqaraleh FA. Comparative Analysis of Advanced Data Mining Methods for Enhancing Medical Diagnosis and Prognosis. Data Metadata. 2024;3:465.
10. 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.
11. Hettiarachchi C, Chitraranjan C. A machine learning approach to predict diabetes using short recorded photoplethysmography and physiological characteristics. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2019. p. 322–7.
12. Noida G, Engineering C, Noida G. An Analysis of Predicting Diabetes using Machine Learning. null. 2018;4(3):16–26.
13. Poly TN, Islam MM, Li YCJ. Early Diabetes Prediction: A Comparative Study Using Machine Learning Techniques. In: Studies in Health Technology and Informatics. 2022. p. 409–13.
14. Joshi RD, Dhakal CK. Predicting type 2 diabetes using logistic regression and machine learning approaches. Int J Environ Res Public Health. 2021;18(14).
15. Reshmi S, Biswas SK, Boruah AN, Thounaojam DM, Purkayastha B. Diabetes Prediction Using Machine Learning Analytics. In: 2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing, COM-IT-CON 2022. 2022. p. 108–12.
16. Samet S, Laouar MR, Bendib I, Eom S. Analysis and Prediction of Diabetes Disease Using Machine Learning Methods. Int J Decis Support Syst Technol. 2022;14(1).
17. Tasin I, Nabil TU, Islam S, Khan R. Diabetes prediction using machine learning and explainable AI techniques. Healthc Technol Lett. 2023;10(1–2):1–10.
18. Bhat SS, Ansari GA. Predictions of diabetes and diet recommendation system for diabetic patients using machine learning techniques. In: 2021 2nd International Conference for Emerging Technology, INCET 2021. 2021.
19. Das A, Das SK, Das D, Alam KMR. A Comparative Study to Predict Diabetes using Machine Learning Techniques. In: 2021 International Conference on Science and Contemporary Technologies, ICSCT 2021. 2021.
20. Bhat SS, Selvam V, Ansari GA, Ansari MD, Rahman MH. Prevalence and Early Prediction of Diabetes Using Machine Learning in North Kashmir: A Case Study of District Bandipora. Comput Intell Neurosci. 2022;2022.
21. Laila UE, Mahboob K, Khan AW, Khan F, Taekeun W. An Ensemble Approach to Predict Early-Stage Diabetes Risk Using Machine Learning: An Empirical Study. Sensors. 2022;22(14).
22. Hu Y, Zheng L, Wang J. Predicting ICU Length of Stay for Patients with Diabetes Using Machine Learning Techniques. In: Proceedings of the International Conference on Cyber-Physical Social Intelligence, ICCSI 2022. 2022. p. 417–22.
23. Liu Q, Zhou Q, He Y, Zou J, Guo Y, Yan Y. Predicting the 2-Year Risk of Progression from Prediabetes to Diabetes Using Machine Learning among Chinese Elderly Adults. J Pers Med. 2022;12(7).
24. Ahmad HF, Mukhtar H, Alaqail H, Seliaman M, Alhumam A. Investigating health-related features and their impact on the prediction of diabetes using machine learning. Appl Sci. 2021;11(3):1–18.
25. Kesavan SP, Rajeswari R. Analysis of Power and Delay in CMOS Universal Gates using Single Virtual Rail Clamping Technique. Asian J Res Soc Sci Humanit. 2016;6(10):68.
26. Ye J, Yao L, Shen J, Janarthanam R, Luo Y. Predicting mortality in critically ill patients with diabetes using machine learning and clinical notes. BMC Med Inform Decis Mak. 2020;20.
27. 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.
28. Wahed MA, Alqaraleh M, Alzboon MS, Al-Batah MS. Evaluating AI and Machine Learning Models in Breast Cancer Detection: A Review of Convolutional Neural Networks (CNN) and Global Research Trends. LatIA. 2025;3:117.
29. 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.
30. 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).
31. 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.
32. Alzboon MS. Internet of things between reality or a wishing-list: a survey. Int J Eng & Technol. 2018;7(2):956–61.
33. 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. 2023.
34. Alzboon MS, Aljarrah E, Alqaraleh M, Alomari SA. Nodexl Tool for Social Network Analysis. Vol. 12, Turkish Journal of Computer and Mathematics Education. 2021.
35. 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.
36. 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 Technologies and Applications, eSmarTA 2023. 2023.
37. Alzboon M. Semantic Text Analysis on Social Networks and Data Processing: Review and Future Directions. Inf Sci Lett. 2022;11(5):1371–84.
38. Alzboon MS. Survey on Patient Health Monitoring System Based on Internet of Things. Inf Sci Lett. 2022;11(4):1183–90.
39. Alzboon M, Alomari SA, Al-Batah MS, Banikhalaf M. The characteristics of the green internet of things and big data in building safer, smarter, and sustainable cities. Int J Eng & Technol. 2017;6(3):83–92.
40. 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.
41. 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.
42. 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.
43. Alzu’bi A, Najadat H, Eyadat W, Al-Mohtaseb A, Haddad H. A new approach for detecting eosinophils in the gastrointestinal tract and diagnosing eosinophilic colitis. Int Arab J Inf Technol. 2021;18(4):596–603.
44. Vasudevan P, Ekambaram C. HYAQP: A Hybrid Meta-Heuristic Optimization Model for Air Quality Prediction Using Unsupervised Machine Learning Paradigms. Int Arab J Inf Technol. 2024;21(5):953–66.
45. Gupta S, Sharma N. Machine Learning Driven Threat Identification to Enhance FANET Security using Genetic Algorithm. Int Arab J Inf Technol. 2024;21(4):711–22.
46. Baradieh K, Zainuri M, Kamari M, Yusof Y, Abdullah H, Zaman M, et al. RSO based Optimization of Random Forest Classifier for Fault Detection and Classification in Photovoltaic Arrays. Int Arab J Inf Technol. 2024;21(4):636–60.
47. Zhu Q, Peng Y. Semi-Supervised Kernel Discriminative Low-Rank Ridge Regression for Data Classification. Int Arab J Inf Technol. 2024;21(5):800–14.
48. Alawneh H, Hasasneh A. Survival Prediction of Children after Bone Marrow Transplant Using Machine Learning Algorithms. Int Arab J Inf Technol. 2024;21(3):394–407.
49. Al-Hinawi A, Alelaimat R. Using Deep Learning for Profitable Concrete Forecasting Methods. Int Arab J Inf Technol. 2024;21(5):832–43.
50. Venkatamune N, Prabhashankar J. A VANET Collision Warning System with Cloud Aided Pliable Q-Learning and Safety Message Dissemination. Int Arab J Inf Technol. 2023;20(1):113–24.
51. Jambunathan SP, Ramadass S, Selva Kumaran PR. Analyzing the Behavior of Multiple Dimensionality Reduction Algorithms to Obtain Better Accuracy using Benchmark KDD CUP Dataset. Int Arab J Inf Technol. 2022;19(1):121–31.
52. Chinnaiah V, Kiliroor CC. Heterogeneous Feature Analysis on Twitter Data Set for Identification of Spam Messages. Int Arab J Inf Technol. 2022;19(1):38–44.
53. Mousa A, Auth T, Samara A, Odeh S. Random Walk Generation and Classification Within an Online Learning Platform. Int Arab J Inf Technol. 2022;19(Special Issue 3A):536–43.
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Copyright (c) 2025 Mowafaq Salem Alzboon , Muhyeeddin Alqaraleh , Mohammad Subhi Al-Batah (Author)
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