Disease Diagnosis Using Machine Learning

Abstract

The rapid growth of healthcare technologies and artificial intelligence has significantly improved the development of intelligent medical assistance systems. Traditional disease diagnosis methods often require considerable time, medical expertise, and physical consultation, which may not always be easily accessible to users. This paper presents the design and implementation of an AIPowered Disease Diagnosis System aimed at improving the efficiency, accuracy, and accessibility of preliminary healthcare analysis. The proposed system integrates machine learning techniques, particularly the Random Forest algorithm, with modern full-stack web technologies to enable effective disease prediction based on user-input symptoms. The system provides real-time disease prediction through an interactive and user-friendly interface supporting symptom selection, prediction analysis, chatbot assistance, analytics visualization, prediction history tracking, and secure authentication mechanisms. The application is developed using React, Tailwind CSS, Node.js, Flask, and MongoDB, ensuring scalability, modularity, responsive design, and secure API communication. Experimental evaluation demonstrates that the system performs efficiently in terms of prediction accuracy, response time, usability, and overall system integration. The proposed platform highlights the practical application of artificial intelligence and machine learning in modern healthcare systems and improves accessibility to preliminary disease diagnosis services.