Affiliation:
1. Institute of Engineering and Management, University of Engineering and Management, Kolkata, India
Abstract
Disease prediction is vital for effective treatment decisions in healthcare organizations. This work focuses on predicting multiple diseases using an improvised Machine Learning concept called 'Apna Clinic.' The system analyzes patient health records to forecast diseases like diabetes, breast cancer, heart disease, kidney disease, and liver disease using data normalization and weighted feature extraction. Comparison with existing models and comprehensive error analysis ensure accurate predictions. The behavior model is stored and deployed via the Flask API, enabling reliable functionality. Users access the system, submit disease parameters, and receive their health status. The analysis helps identify serious diseases, monitor patients, and provide timely warnings or suggestions for treatment. In acute cases, the system locates specialized doctors nearby. Additionally, a disease compendium with information on symptoms, prevention, and treatment is provided. The aim is to enhance treatment decisions, empower individuals, and facilitate proactive healthcare actions.