AI-Based Disease Detection System

Abstract

The rapid growth of Artificial Intelligence (AI) and Machine Learning (ML) technologies has significantly transformed the healthcare sector by enabling intelligent and automated medical solutions. The proposed AI-Based Disease Detection System is developed to predict diseases based on symptoms entered by users through a web-based application. The main objective of this system is to provide fast, accurate, and efficient preliminary disease prediction that can assist both users and healthcare professionals in early diagnosis.

The system uses machine learning algorithms such as Decision Tree, Naïve Bayes, and Support Vector Machine (SVM) to analyze symptom data and identify the most probable disease. Medical datasets containing symptoms and disease information are collected, preprocessed, and used for training and testing the prediction models. The application is implemented using Python and Flask for backend processing, while HTML and CSS are used for developing the frontend interface.

The developed system provides a user-friendly environment where users can enter symptoms and receive prediction results within a short time. The proposed model helps reduce manual effort, improves accessibility to healthcare support, and assists in early disease identification. Experimental analysis shows that the system achieves reliable prediction accuracy and efficient performance.

This project demonstrates the effective application of Artificial Intelligence in healthcare and highlights the potential of machine learning techniques in improving disease prediction systems and supporting modern medical services.