International Journal of Science, Engineering and TechnologyAuthors: Assistant Professor Dr Rajkumar, Mehreen, Abiha kazmi, Km Ilma, Apeksha Kaushik, Kritika
Abstract: The goal of this project is to develop a smart system that can forecast illnesses like typhoid, dengue, and malaria by analyzing patient-reported symptoms. It uses three different machine learning algorithms—Random Forest, Decision Tree, and Support Vector Machine—to identify diseases from a predefined list of symptoms and the corresponding diagnosis. Every algorithm has undergone rigorous training and testing to guarantee its precision and dependability in forecasting. When it comes to disease prediction, the Random Forest method outperforms the Decision Tree and Support Vector Machine models. By accurately identifying the relevant illness based on the user's symptoms, it consistently yields reliable results, making it the most effective model in our system.
