Automated Diabetes Risk Assessment Using Machine Learning Algorithms

G. Swathi; A. Swathi; M. Tharun Kumar Reddy; M. Sravanthi; S. Yatheesha; P. Vikram Simha Reddy; A. Prashanth

doi:https://doi.org/10.47001/ICJES/2025.402002

Authors

G. Swathi

Department of Computer Science Engineering (Artificial Intelligence), Gates Institute of Technology, Gooty, Andhra Pradesh, India

A. Swathi

Department of Computer Science Engineering (Artificial Intelligence), Gates Institute of Technology, Gooty, Andhra Pradesh, India

M. Tharun Kumar Reddy

Department of Computer Science Engineering (Artificial Intelligence), Gates Institute of Technology, Gooty, Andhra Pradesh, India

M. Sravanthi

Department of Computer Science Engineering (Artificial Intelligence), Gates Institute of Technology, Gooty, Andhra Pradesh, India

S. Yatheesha

Department of Computer Science Engineering (Artificial Intelligence), Gates Institute of Technology, Gooty, Andhra Pradesh, India

P. Vikram Simha Reddy

Department of Computer Science Engineering (Artificial Intelligence), Gates Institute of Technology, Gooty, Andhra Pradesh, India

A. Prashanth

Department of Computer Science Engineering (Artificial Intelligence), Gates Institute of Technology, Gooty, Andhra Pradesh, India

Abstract

Diabetes Mellitus is among critical diseases and lots of people are suffering from this disease. Age, obesity, lack of exercise, hereditary diabetes, living style, bad diet, high blood pressure, etc. can cause Diabetes Mellitus. People having diabetes have high risk of diseases like heart disease, kidney disease, stroke, eye problem, nerve damage, etc. Current practice in hospital is to collect required information for diabetes diagnosis through various tests and appropriate treatment is provided based on diagnosis. Big Data Analytics plays an significant role in healthcare industries. Healthcare industries have large volume databases. Using big data analytics one can study huge datasets and find hidden information, hidden patterns to discover knowledge from the data and predict outcomes accordingly. In existing method, the classification and prediction accuracy is not so high. In this Project, we have proposed a diabetes prediction model for better classification of diabetes which includes few external factors responsible for diabetes along with regular factors like Glucose, BMI, Age, Insulin, etc. Classification accuracy is boosted with new dataset compared to existing dataset. Further with imposed a pipeline model for diabetes prediction intended towards improving the accuracy of classification.

Keywords

Diabetes Prediction Machine Learning Classification Algorithms Artificial Intelligence Healthcare Analytics

Citation of this Article

G. Swathi, A. Swathi, M. Tharun Kumar Reddy, M. Sravanthi, S. Yatheesha, P. Vikram Simha Reddy, A. Prashanth. (2025). Automated Diabetes Risk Assessment Using Machine Learning Algorithms. International Current Journal of Engineering and Science - ICJES, 4(2), 6-11. Article DOI: https://doi.org/10.47001/ICJES/2025.402002

References

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[1] Sharma, A., & Parmar, D. (2020). Diabetes prediction using machine learning techniques. Materials Today: Proceedings, 33, 3738–3743.

[2] Muhammad, L. J., Algehyne, E. A., Usman, S. S., Yusuf, A., & Ahmad, A. (2020). Supervised machine learning models for prediction of diabetes mellitus. SN Computer Science, 1(6), 1–10.

[3] Vakil, V. et al. (2021). Explainable predictions of different machine learning algorithms used to predict Early Stage diabetes. arXiv preprint arXiv:2111.09939.

[4] Rana, R., & Sharma, D. (2021). Early diagnosis and prediction of diabetes using machine learning techniques. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 7(2), 2456–3307.

[5] Dritsas, E., & Trigka, M. (2022). Data-Driven Machine-Learning Methods for Diabetes Risk Prediction. Sensors, 22(14), 5304.

[6] Imrie, F. et al. (2022). Auto Prognosis 2.0: Democratizing Diagnostic and Prognostic Modeling in Healthcare with Automated Machine Learning. arXiv preprint arXiv:2210.12090.

[7] Khan, M. Y. et al. (2022). An AI-based approach for early diabetes prediction using machine learning techniques. Computers in Biology and Medicine, 141, 105004.

[8] Abegaz, T. M. et al. (2023). Application of Machine Learning Algorithms to Predict Uncontrolled Diabetes Using the All of Us Research Program Data. Healthcare, 11(8), 1138.

[9] Kakoly, I. J., Hoque, M. R., & Hasan, N. (2023). Data-Driven Diabetes Risk Factor Prediction Using Machine Learning Algorithms with Feature Selection Technique. Sustainability, 15(6), 4930.

[10] Kumar, A., & Rajasekaran, C. (2023). A hybrid ensemble approach for diabetes prediction using ML algorithms. Informatics in Medicine Unlocked, 38, 101138.

[11] Gupta, P., & Sindhu, R. (2024). Diabetes Prediction Using Machine Learning. Journal of Electrical Systems, 20(7s).

[12] Karmand, M. et al. (2024). Machine‐learning algorithms in screening for type 2 diabetes mellitus: Data from Fasa Adults Cohort Study. Endocrinology, Diabetes & Metabolism.