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AI POWERED DIABETES AND HEART DISEASE RISK PREDICTOR

AUTHORS:
MAGESHWARAN M
VASANTHAKUMAR B
ASHWATH RAJ R
Mentor
Dr S SATHYA
Affiliation
DOI: 10.55041/ijsmt.v2i5.036
CC BY 4.0 License:
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Abstract

AI-POWERED DIABETES & HEARTDISEASERISK PREDICTOR is a web-based application that uses artificial intelligence to predict the risk of diabetes and heart disease based on user-provided health parameters such as age, BMI, blood pressure, blood sugar, cholesterol levels, and lifestyle habits. Users enter their data through an interactive web form built with HTML, CSS, and JavaScript, and the system it using a machine learning model, either in-browser or via a backend API. The results are displayed visually with color-coded risk levels and charts, making it easy to understand, and the application can also provide preventive suggestions for high-risk users. This project combines Al and web development, offering a practical tool for early detection and promoting preventive healthcare. This project not only demonstrates practical Al and web development skills but also addresses a critical healthcare need by promoting early detection, raising health awareness, and encouraging proactive preventive measures, making it both technically impressive and socially impactful. Early detection of lifestyle diseases such as diabetes and cardiovascular disease is crucial, as these conditions often develop silently without obvious symptoms in initial stages. Many individuals fail to undergo regular health check-ups due to lack of awareness, cost, or accessibility. This project aims to bridge that gap by providing an AI-based predictive system that can estimate disease risk instantly using basic health parameters, enabling users to take preventive action at an early stage.

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PUBLICATION DETAILS
Review Type:
Double-Blind Peer Review
License:
CC BY-NC 4.0
Volume/Issue:
Volume 02, Issue 05
Review Rounds:
2
Article Type:
Research Article
Publication Date:
May 05 2026
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M, M., B, V. & R, A. R. (2026). AI Powered Diabetes and Heart Disease Risk Predictor. International Journal of Science, Strategic Management and Technology, 02(05). https://doi.org/10.55041/ijsmt.v2i5.036

M, MAGESHWARAN, et al.. "AI Powered Diabetes and Heart Disease Risk Predictor." International Journal of Science, Strategic Management and Technology, vol. 02, no. 05, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i5.036.

M, MAGESHWARAN,VASANTHAKUMAR B, and ASHWATH R. "AI Powered Diabetes and Heart Disease Risk Predictor." International Journal of Science, Strategic Management and Technology 02, no. 05 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i5.036.

References
1.Ponderosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blonde, M., Peterhead, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, E. (2011). Scikit-learn: Machine  Learning  in Journal of Machine Learning Research, 12, 2825–2830.

2.Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied Logistic Regression (3rd). Wiley.

3.World Health Organization. (2021). Cardiovascular diseases (CVDs) Fact Sheet. Retrieved from https://www.who.int Communicationand Networking Technologies (pp. 1–5). IEEE.

4.UCI Machine Learning Repository. (1988). Heart Disease Dataset. University of California, Irvine. Retrieved from https://archive.ics.uci.edu/ml/datasets/heart+ Disease

5.(2016). Pima Indians Diabetes Database. Retrieved from https://www.kaggle.com/datasets/uciml/pim a-indians-diabetes-database

6.(2020). Heart Disease UCI Dataset. Retrieved from https://www.kaggle.com/datasets/ronitf/hear t-disease-uci

7.Chowdhury, E., & Rahman, M. (2022). Predictive analysis of cardiovascular disease using machine learning algorithms. IEEE Access, 10, 112345–112356.

8.Gandhi, R., & Singh, A. (2021). Diabetes prediction using machine learning techniques. In Proceedings of the International Conference on Intelligent Systems (pp. 210–215). IEEE.

9.Aljaaf, A. J., Al-Jumeily, D., & Hussain,(2018). Early prediction of type 2 diabetes using data mining techniques. Journal of Medical Systems, 42(12)Kumar, N., & Sinha, R. (2019). Heart disease prediction system using machine learning algorithms. In Proceedings of International  Conference  on  Computing
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This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
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