Diabetes Prediction Using Medical Data and Disease Influence Measures using Machine Learning


  • Avinash J. Agrawal Shri Ramdeobaba College of Engineering and Management, Nagpur, Maharashtra, India
  • Rashmi R. Welekar Shri Ramdeobaba College of Engineering and Management, Nagpur, Maharashtra, India
  • Namita Parati Maturi Venkata Subba Rao (MVSR) Engineering College, Hyderabad, Telangana State, India
  • Pravin R. Satav Government Polytechnic, Murtijapur, Maharashtra, India
  • Leena H. Patil Priyadarshini College of Engineering, Nagpur, Maharashtra, India
  • Shailendra S. Aote Shri Ramdeobaba College of Engineering and Management, Nagpur, Maharashtra, India


Support Vector Machine, Decision Trees, Random Forests, machine learning for diabetes prediction


This project intends to create a diabetes predictive model utilizing medical data and investigate the impact of various factors on the condition using machine learning techniques. Millions of individuals throughout the world suffer from the common chronic illness known as diabetes. The results of patients and public health initiatives can be considerably improved by early detection and an understanding of the underlying causes.To do this, a large dataset of medical records from people with and without diabetes that included a variety of demographic, lifestyle, and clinical factors was gathered. To pre-process the data and identify useful features, feature engineering approaches were used. Accurate prediction models for diabetes risk assessment were created using a variety of machine learning methods, such as Decision Trees, Random Forests, and Support Vector Machines.The main causes of the development of diabetes were also determined by looking into disease influence measures. This study intends to clarify the relative importance of several risk factors, such as age, BMI, family history, and glucose levels, by examining feature importance and correlation coefficients.In this paper various disease prediction methods were assessed and contrasted depending on how well they predicted diseases. The analysis' findings have been given in great detail to aid in the development process.


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Moghissi, E.S.; Korytkowski, M.T.; DiNardo, M.; Einhorn, D.; Hellman, R.; Hirsch, I.B.; Inzucchi, S.E.; Ismail-Beigi, F.; Kirkman, M.S.; Umpierrez, G.E. (2009). "American Association of Clinical Endocrinologists and American Diabetes Association consensus statement on inpatient glycemic control." Diabetes Care, 32, 1119–1131.

Borgharkar, S.S.; Das, S.S. (2019). "Real-world evidence of glycemic control among patients with type 2 diabetes mellitus in India: The TIGHT study." BMJ Open Diabetes Res. Care, 7, e000654.

S. N. Ajani and S. Y. Amdani, "Probabilistic path planning using current obstacle position in static environment," 2nd International Conference on Data, Engineering and Applications (IDEA), 2020, pp. 1-6, doi: 10.1109/IDEA49133.2020.9170727.

S. Ajani and M. Wanjari, "An Efficient Approach for Clustering Uncertain Data Mining Based on Hash Indexing and Voronoi Clustering," 2013 5th International Conference and Computational Intelligence and Communication Networks, 2013, pp. 486-490, doi: 10.1109/CICN.2013.106.

Fang, M.; Wang, D.; Coresh, J.; Selvin, E. (2021). "Trends in diabetes treatment and control in US adults, 1999–2018." New Engl. J. Med., 384, 2219–2228.

Raveendran, A.V.; Chacko, E.C.; Pappachan, J.M. (2018). "Non-pharmacological treatment options in the management of diabetes mellitus." Eur. Endocrinol., 14, 31.

American Diabetes Association. (2021). "Pharmacologic approaches to glycemic treatment: Standards of Medical Care in Diabetes—2021." Diabetes Care, 44(Suppl. S1), S111–S112.

Garg, S.K.; Grunberger, G.; Weinstock, R.S.; Lawson, M.L.; Hirsch, I.B.; DiMeglio, L.A.; Pop-Busui, R.; Philis-Tsimikas, A.; Kipnes, M.S.; Lilenquist, D.R. (2023). "Improved Glycemia with Hybrid Closed-Loop (HCL) Versus Continuous Subcutaneous Insulin Infusion (CSII) Therapy: Results from a Randomized Controlled Trial." Diabetes Technol. Ther., 25, 1–12.

Phillip, M.; Nimri, R.; Bergenstal, R.M.; Barnard-Kelly, K.; Danne, T.; Hovorka, R.; Kovatchev, B.P.; Messer, L.H.; Parkin, C.G.; Ambler-Osborn, L. (2022). "Consensus Recommendations for the Use of Automated Insulin Delivery (AID) Technologies in Clinical Practice." Endocr. Rev., 44, 254–280.

Borkar, P., Wankhede, V.A., Mane, D.T. et al. Deep learning and image processing-based early detection of Alzheimer disease in cognitively normal individuals. Soft Comput (2023). https://doi.org/10.1007/s00500-023-08615-w

Ajani, S.N., Mulla, R.A., Limkar, S. et al. DLMBHCO: design of an augmented bioinspired deep learning-based multidomain body parameter analysis via heterogeneous correlative body organ analysis. Soft Comput (2023). https://doi.org/10.1007/s00500-023-08613-y

Battelino, T.; Alexander, C.M.; Amiel, S.A.; Arreaza-Rubin, G.; Beck, R.W.; Bergenstal, R.M.; Buckingham, B.A.; Carroll, J.; Ceriello, A.; Chow, E. (2022). "Continuous glucose monitoring and metrics for clinical trials: An international consensus statement." Lancet Diabetes Endocrinol., 11, 42–57.

Pantalone, K.M.; Misra-Hebert, A.D.; Hobbs, T.M.; Wells, B.J.; Kong, S.X.; Chagin, K.; Dey, T.; Milinovich, A.; Weng, W.; Bauman, J.M. (2018). "Effect of glycemic control on the Diabetes Complications Severity Index score and development of complications in people with newly diagnosed type 2 diabetes." J. Diabetes, 10, 192–199.

Pettus, J.H.; Zhou, F.L.; Shepherd, L.; Preblick, R.; Hunt, P.R.; Paranjape, S.; Miller, K.M.; Edelman, S.V. (2019). "Incidences of severe hypoglycemia and diabetic ketoacidosis and prevalence of microvascular complications stratified by age and glycemic control in US adult patients with type 1 diabetes: A real-world study." Diabetes Care, 42, 2220–2227.

Basu, S.; Narayanaswamy, R. (2019). "A prediction model for uncontrolled type 2 diabetes mellitus incorporating area-level social determinants of health." Med. Care, 57, 592–600.

Chatterjee, R.; Yeh, H.C.; Edelman, D.; Brancati, F. (2011). "Potassium and risk of Type 2 diabetes." Expert Rev. Endocrinol. Metab., 6, 665–672.

Jian, Y.; Pasquier, M.; Sagahyroon, A.; Aloul, F. (2021). "A Machine Learning Approach to Predicting Diabetes Complications." Healthcare, 9, 1712.

Dinh, A.; Miertschin, S.; Young, A.; Mohanty, S.D. (2019). "A data-driven approach to predicting diabetes and cardiovascular disease with machine learning." BMC Med. Inform. Decis. Mak., 19, 211.

Zou, Q.; Qu, K.; Luo, Y.; Yin, D.; Ju, Y.; Tang, H. (2018). "Predicting diabetes mellitus with machine learning techniques." Front. Genet., 9, 515.

Yang, L.; Gabriel, N.; Hernandez, I.; Winterstein, A.G.; Guo, J. (2021). "Using machine learning to identify diabetes patients with canagliflozin prescriptions at high-risk of lower extremity amputation using real-world data." Pharmacoepidemiol. Drug Saf., 30, 644–651.

Del Parigi, A.; Tang, W.; Liu, D.; Lee, C.; Pratley, R. (2019). "Machine learning to identify predictors of glycemic control in type 2 diabetes: An analysis of target HbA1c reduction using empagliflozin/linagliptin data." Pharm. Med., 33, 209–217.

Seo, W.; Lee, Y.-B.; Lee, S.; Jin, S.-M.; Park, S.-M. (2019). "A machine-learning approach to predict postprandial hypoglycemia." BMC Med. Inform. Decis. Mak., 19, 210.

Hanson, R.L.; Imperatore, G.; Bennett, P.H.; Knowler, W.C. (2002). "Components of the “metabolic syndrome” and incidence of type 2 diabetes." Diabetes, 51, 3120–3127.

Bhutto, A.R.; Abbasi, A.; Abro, A.H. (2019). "Correlation of hemoglobin A1c with red cell width distribution and other parameters of red blood cells in type II diabetes mellitus." Cureus, 11, e5533.

All of Us Research Program Investigators. (2019). "The “All of Us” research program." N. Engl. J. Med., 381, 668–676.

Ramirez, A.H.; Sulieman, L.; Schlueter, D.J.; Halvorson, A.; Qian, J.; Ratsimbazafy, F.; Loperena, R.; Mayo, K.; Basford, M.; Deflaux, N. (2022). "The All of Us Research Program: Data quality, utility, and diversity." Patterns, 3, 100570.

R Core Team. (2022). "R: A Language and Environment for Statistical Computing." R Foundation for Statistical Computing: Vienna, Austria. Available online: https://www.R-project.org/ (accessed on 13 April 2023).

Menardi, G.; Torelli, N. (2014). "Training and assessing classification rules with imbalanced data." Data Min. Knowl. Discov., 28, 92–122.

Fan, Y.; Long, E.; Cai, L.; Cao, Q.; Wu, X.; Tong, R. (2021). "Machine learning approaches to predict risks of diabetic complications and poor glycemic control in nonadherent type 2 diabetes." Front. Pharmacol., 12, 1485.

Motaib, I.; Aitlahbib, F.; Fadil, A.; Tlemcani, F.Z.R.; Elamari, S.; Laidi, S.; Chadli, A. (2022). "Predicting poor glycemic control during Ramadan among non-fasting patients with diabetes using artificial intelligence based machine learning models." Diabetes Res. Clin. Pract., 190, 109982.

Tao, X.; Jiang, M.; Liu, Y.; Hu, Q.; Zhu, B.; Hu, J.; Guo, W.; Wu, X.; Xiong, Y.; Shi, X. (2022). "Predicting three-month fasting blood glucose and glycatedhemoglobin of patients with type 2 diabetes based on multiple machine learning algorithms." Research Square.

Coregliano-Ring, L.; Goia-Nishide, K.; Rangel, É.B. (2022). "Hypokalemia in Diabetes Mellitus Setting." Medicina, 58, 431.

Gulati, M. ., Yadav, R. K. ., & Tewari, G. . (2023). Physiological Conditions Monitoring System Based on IoT. International Journal on Recent and Innovation Trends in Computing and Communication, 11(4s), 199–202. https://doi.org/10.17762/ijritcc.v11i4s.6514

Prof. Parvaneh Basaligheh. (2017). Design and Implementation of High Speed Vedic Multiplier in SPARTAN 3 FPGA Device. International Journal of New Practices in Management and Engineering, 6(01), 14 - 19. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/51




How to Cite

Agrawal, A. J. ., Welekar, R. R. ., Parati, N. ., Satav, P. R. ., Patil, L. H. ., & Aote, S. S. . (2023). Diabetes Prediction Using Medical Data and Disease Influence Measures using Machine Learning. International Journal of Intelligent Systems and Applications in Engineering, 11(10s), 01–10. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3229



Research Article