Multiple Disease Prediction System using Machine Learning

Abstract

The goal of this project is to create a machine learning-based system that is inclusive and can predict a number of chronic illnesses, including diabetes, heart disease, and chronic kidney disease. In order to determine which machine learning classification technique is best for disease prediction, this study uses a variety of models, including K- Nearest Neighbour, Support Vector Machine, Decision Tree, Random Forest, and Logistic Regression. These models are tested using numerous disease-specific datasets to ensure accuracy, sensitivity, and specificity. It is important to identify chronic diseases early to increase the patient's outcome as well as reduce the death rate. The present work aims to develop predictive models that can help identify potential at-risk individuals for chronic conditions through the analysis of patient data, including their medical history, demographics, and clinical measurements. The end product would be a web application that helps in early diagnosis, better patient care, and effective usage of healthcare resources. This research proves the capability of machine learning to diagnose chronic diseases at an early stage.