A Machine Learning Based Approach to Personalized Health and Wellness

Advancements in artificial intelligence and machine learning have revolutionized healthcare by enabling precise and personalized medical solutions. This paper leverages machine learning techniques to predict diseases based on user-reported symptoms, analysing vast medical data to identify health conditions accurately. It employs supervised learning algorithms such as support vector classifier (SVC), random forest, gradient boosting, K-Nearest Neighbors (KNN), and multinomial naïve bayes (MultinomialNB) to recognize symptom-disease patterns. Natural language processing and classification techniques enhance prediction accuracy, while evidence-based recommendations provide personalized physical exercises and medication guidance. This AI-driven approach promotes early diagnosis, preventive healthcare, and patient empowerment, bridging the gap between individuals and medical professionals for proactive health management. The proposed work accomplishes more different machine learning algorithms compared to existing systems and gets better accuracy with an additional feature that is providing them the necessary diet and workouts.