Building A Symptom-Based Disease Diagnosis Web App with Flask and Machine Learning

Abstract

Building a symptom-based disease diagnosis web application built using Flask and machine learning, designed to assist users in identifying potential health conditions based on reported symptoms. The system leverages a trained machine learning model to analyze symptom data and predict possible diseases, providing a preliminary diagnosis and guiding users toward seeking appropriate medical advice. The algorithms used in various prediction system consisted of Linear Regression, Decision Tree, Naïve Bayes, KNN, Random Forest Tree, etc. by using these it is possible to predict more than one disease at a time. So, the user does not need to traverse many models to predict the diseases. The model employs algorithms optimized for multi-class classification, capable of handling complex symptom-disease relationships to improve diagnostic precision. Flask, a lightweight yet powerful web framework, serves as the application’s backbone, providing a responsive interface that facilitates symptom input, rapid data processing, and real-time display of diagnosis results, ensuring a smooth user experience. Beyond basic diagnosis, the application offers a range of functionalities aimed at enhancing user engagement and education, including detailed information about description, precaution, medication, workout and diet related to that disease.