Healthcare With Predictive Analysis

Diabetes mellitus is a global health epidemic, with early detection being critical to prevent severe complications and reduce healthcare burdens. This project, **Healthcare Predictive Analysis for Diabetes**, addresses this challenge by developing a web-based platform that leverages machine learning (ML) to predict diabetes risk using clinical and demographic data. Built with **Python Django** for the backend and **CSS Bootstrap** for the frontend, the system combines robust predictive analytics with an intuitive, responsive user interface tailored for healthcare professionals. The platform utilizes the **Pima Indians Diabetes Dataset**, which includes features such as glucose levels, BMI, insulin resistance, and age. Machine learning algorithms like **Random Forest**, **Logistic Regression**, and **XGBoost** are implemented using Python’s Scikit- learn library to train models that achieve an accuracy of **89%** and an **AUC-ROC score of 0.92**, demonstrating strong predictive performance. The Django backend facilitates secure data management, RESTful API integration, and real-time predictions, while Bootstrap ensures a mobile-friendly, visually appealing frontend with dynamic dashboards for visualizing risk factors