• GAUTAM PRASHANTKUMAR MEHTA
• TRUPTI ASOLKAR

Lifestyle-related and chronic diseases are increasingly determining health. Risk estimation at an early stage is vital not only for prevention and planning treatment but also in order to cut healthcare costs. Much of the previous work has focused on the prediction of a single disease with limited practical usefulness in real-world applications [1], [2]. This article introduces a web-based tool for predictive analysis that employs machine learning to predict risks for more than one disease like diabetes and heart disease. The system takes very limited inputs such as age, blood pressure, glucose level, and BMI to make quick predictions. We employed Logistic Regression, Random Forest, and XGBoost models trained on public databases [3], [4]. The system provides risk levels across disease in an easy-to-use web interface, filling the gap between research single-disease models and practical multi-disease needs

Disease Prediction, Basic Disease Prediction, Chronic Disease Prediction, Web-based Framework.

"A WEB-BASED FRAMEWORK FOR MULTIPLE DISEASE PREDICTION USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 9, page no.c586-c589, September-2025, Available :http://www.jetir.org/papers/JETIR2509266.pdf

"A WEB-BASED FRAMEWORK FOR MULTIPLE DISEASE PREDICTION USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 9, page no. ppc586-c589, September-2025, Available at : http://www.jetir.org/papers/JETIR2509266.pdf