Machine Learning-Based Intelligent Healthcare System for Accurate Disease Prediction and Treatment Recommendation

Early disease identification is crucial for prompt treatment and improved patient outcomes in the modern healthcare system. This study presents a machine learning-based disease prediction and medical recommendation system that analyses symptoms and offers precise health insights. A well-structured dataset comprising disease categories, suggested therapies, and symptom severity forms the foundation of the system. It provides individualized nutritional and medical advice by using a classification-based predictive model to analyse symptom patterns and propose potential diagnoses. We have created a web-based interface to make the system easier to use, enabling users to enter their symptoms and get immediate medical advice. This method allows users to conduct a preliminary self-evaluation prior to requesting expert assistance, thereby bridging the gap between patients and medical advice. This technology provides rapid and accurate health insights, making it an invaluable resource for well-informed healthcare decision-making. Keywords- Disease Prediction System, Machine Learning in Healthcare, Random Forest Classifier, Flask Web Application, Symptom-Based Diagnosis, Medical Recommendations