Enhancing Disease Prediction Through Symptoms Based Machine Learning

Traditional approaches to diagnosing and treating illnesses may have limitations, particularly when it comes to serious diseases. Therefore, it is essential to have precise and timely analysis of health issues for effective prevention and treatment. Machine learning (ML) algorithms offer a valuable tool in the development of a medical diagnosis system that can provide more accurate disease predictions compared to conventional methods. Our team has successfully developed a disease prediction system using a variety of ML algorithms, including Decision trees, Random Forest, Naïve Bayes, and Logistic Regression. This system leverages a dataset containing 4962 entries, encompassing 132 symptoms and over 300 diseases. By employing a rule-based approach, we have created a framework that enables the development and application of predictive models. The system's accuracy is remarkable, empowering clinicians to anticipate and analyze ailments at an early stage, thereby addressing health-related challenges more effectively