In recent years, the application of Machine Learning (ML) in the healthcare sector has significantly improved the accuracy and efficiency of disease diagnosis. This project presents a Disease Prediction System using Machine Learning that predicts possible diseases based on user-input symptoms. The main objective of the system is to assist users and healthcare professionals in early disease detection and preliminary diagnosis.The system is developed using the Random Forest Classification Algorithm, which is known for its high accuracy and ability to handle large datasets efficiently. A medical dataset containing various symptoms and corresponding diseases is used to train the model. The dataset is preprocessed to remove inconsistencies and improve prediction accuracy. After training, the model is integrated into a web-based application developed using Flask, allowing users to enter symptoms through a user-friendly interface.When a user selects or inputs symptoms, the trained model analyzes the data and predicts the most probable disease. The system aims to reduce the time required for diagnosis and provide an initial assessment before consulting a medical professional. The model achieves high accuracy and demonstrates the effectiveness of machine learning techniques in healthcare applications.
Rewatkar et al. (Wed,) studied this question.