DISEASE RISK PREDICTION SYSTEM USING MACHINE LEARNING

Authors:

Mr. I. Ravi Shireesh Kumar¹

1. Aishwarya², P. Thanmayi Chowdary³, N. Ganesh⁴, G. Nandhini Devi⁵,

 ¹Associate Professor, ^2,3,4,5Student

Department of CSE-AIML, Sreyas Institute of Engineering and Technology,

Hyderabad, India-500068

ABSTRACT

Improving healthcare outcomes and lowering mortality depend on early detection of diabetes and heart disease, but delays are frequently caused by a lack of knowledge and access to medical facilities. A machine learning-based disease risk prediction system that examines fundamental health paramaters like age, gender, lifestyle, symptoms, blood pressure, glucose levels, etc. is proposed in this study. To improve accuracy, the model makes use of publicly accessible datasets and preprocessing methods like feature selection, data cleaning, and normalisation. Early preventive action is made possible by its ability to predict individual risk levels and present results in an easy-to-understand format. The system provides an accessible, cost-effective solution that raises awareness and facilitates well-informed healthcare choices.

Keywords: Disease Risk Prediction, Machine Learning, Diabetes, Heart Disease, Preventive Healthcare