Design and Implementation of an AI-Based Healthcare Disease Prediction System Using Machine Learning

The rapid development and advancement in artificial intelligence and machine learning technologies have created new avenues and frontiers for healthcare, especially for early detection and prediction of diseases. In this paper, we propose the design, development, and implementation of an AI-Based Healthcare Disease Prediction System for predicting various diseases, including diabetes, heart disease, and liver disease, using various supervised machine learning algorithms. The proposed system is designed and implemented using Python programming language and Scikit-learn library for machine learning and Flask framework for backend web development, providing a user-friendly web interface for end users, including patients and healthcare professionals. Various classification algorithms, including Logistic Regression, Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN), have been implemented and tested using benchmark publicly available datasets for medical and healthcare applications. The proposed system is capable of achieving 97.5% accuracy for heart disease, 95.8% for diabetes, and 94.2% for liver disease. The proposed paper outlines various aspects, including system architecture, data preprocessing strategies, API development, and deployment methodology for the proposed system.