Machine Learning-Based Framework For Early Clinical Diagnosis

Abstract: Getting sick is something everyone experiences and figuring out what's wrong can sometimes be tough. Imagine having a smart computer system that could help you understand what might be causing your symptoms and even suggest ways to feel better. This project is all about creating such a system, using computer science to help people with their health. We built a system that takes a list of symptoms someone might have, like "itching" or "fever," and then uses powerful computer programs to guess what disease they might have. We used a special kind of data that lists many symptoms and their related diseases. We trained several "machine learning" models, which are like very smart pattern-spotters, to learn from this data. The most successful model, called SVC (Support Vector Classifier), along with others like RandomForest and Gradient Boosting, showed amazing accuracy, correctly identifying diseases almost every time in our tests. After predicting the disease, our system also provides helpful information like what to do to be careful, what medicines might be used, what foods to eat, and even some exercises. This entire system is packaged into a user-friendly website, making it easy for anyone to get quick, preliminary health information and even generate a basic health report.

Keywords: Artificial Intelligence, Machine Learning, Disease Prediction, Healthcare Recommendation System, Symptom Analysis, Medical Diagnosis, Personalized Medicine