Early Diabetic Prediction System using Machine Learning

The goal of this project is to create a machine learning-based system that is inclusive and can predict many chronic illnesses, including diabetes, heart disease, and chronic kidney disease. To determine which machine learning classification technique is best for disease prediction, this study uses a variety of models, including K-Nearest Neighbours, Support Vector Machines, Decision Trees, Random Forests, 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 improve patient outcomes and reduce mortality. The present work aims to develop predictive models to identify at-risk individuals for chronic conditions by analysing patient data, including medical history, demographics, and clinical measurements. The end product would be a web application that supports early diagnosis, better patient care, and the effective use of healthcare resources. This research helps in proving the capability of machine learning to diagnose chronic diseases at an early stage.