A Novel Machine Learning Approach for Medical Recommendation System

Hospitals are using online recommender systems more and more frequently. Nowadays, the vast majority of consumers research potential prescriptions online before consulting their doctors for a variety of medical issues. When pandemics, floods, occur, the medical recommendation system may come in handy. Using fewer resources, recommender systems provide more precise, dependable, and accurate clinical predictions. The patient receives trustworthy information regarding the medication, dose, and potential side effects from the medication recommendation system. The patient's symptoms are taken into account while choosing the right medication, which is then delivered based on the user profile. This system uses K-means clustering algorithms to analyze patient data and provide personalized medical recommendations. These algorithms use characteristics like patient demographics, medical histories, and symptoms—all of which are gathered from a sizable dataset of medical records— to produce precise suggestions. This system's objective is to enhance patient outcomes by giving fast, precise recommendations that are tailored to each person's particular need.