Health Care Recommender System Using Machine Learning

Health-related information is of significant concern to the public. A study by the Pew Internet and American Life Project found that 60% of adults seek health information online, with 35% attempting to diagnose themselves. Medical errors, often resulting from limited medical expertise, rank among the leading causes of death. In response, a drug recommendation system is proposed to support doctors during prescription processes. Currently, due to the vast amount of clinical data scattered across numerous websites, consumers face difficulties accessing pertinent information to improve their well-being. One major risk that endangers patients is drug-related errors. This emphasizes the need for recommendation systems in healthcare to help patients make informed, cost-effective decisions. Utilizing machine learning algorithms like N-Gram and Lightgbm, the system analyzes medical data to recommend appropriate medications for individual patients. Modules include database management, data preparation, visualization, recommendations, and model evaluation, aiming to reduce errors and improve patient outcomes by integrating technology into healthcare.