JESWIN JOSEPH J

SymptoScan is my pre-final year project in which we created an automated disease prediction system. Users enter a list of symptoms, and the system uses Random Forest, Naive Bayes, and Support Vector Machine models to forecast disease. Individual model forecasts are included in the output, as well as a final consensus prediction for rapid health insights.

This project incorporates major characteristics such as symptom-based analysis, the use of Random Forest, Naive Bayes, and SVM models, a user-friendly interface, disease flexibility, a consensus prediction method, and integrated testing for dependability. It is a valuable tool for both healthcare professionals and people seeking instant health insights, as it is ideal for quick and trustworthy disease forecasts.