Abstract

Personalized medicine recommendation systems are increasing in popularity to predict diseases and provide customized health advice on diet, workout plans and medication. The medical suggestion system can be valuable when pandemics, floods, or cyclones hit. In the age of Machine Learning (ML), recommender systems give more accurate, precise, and reliable clinical predictions while using less resources. Through the use of machine learning algorithms like Decision Tree, Random Forest, K-Means Clustering, and Hierarchical Clustering, these systems analyze patient inputs such as lifestyle data, symptoms, and health metrics for accurate predictions of diseases and holistic recommendations on health. This inclusive process ensures each person receives tailored support to enhance the entire management of their health. The system's ability to suggest accurate diets, proper workouts, and appropriate drugs depending on the condition of the user significantly enhances its contribution toward more healthy lifestyles. The variety of algorithms increases accuracy and reliability as well because each model contributes uniquely in analyzing various aspects of patient data. This study presents a framework that demonstrates the system's efficacy in providing personalized disease predictions and health recommendations, which can benefit the development of preventive care and improve treatment outcomes.