Machine Learning Powered Health Advisory and Web Solutions

Due to unavailability of proper medication, people started taking medicine themselves without appropriate consultation, this making their health condition more worse than usual side effects. Nowadays many online applications are already using the Advisor systems that provides a recommendation for a particular items like movies, music etc. This abstract describes a new method for medicine identification that combines a specialized medicine recommendations for diseases with machine learning algorithms and supports the physical health by suggesting the proper workouts and diets. The given system builds an accurate advising model for medicine detection by utilizing a wide range of characteristics, such as type of disease, data sets, and medicine history. This theory produces a system which gives the best medicine to the users. According to the individual user's rating available in data storage and the description of the diseases. This system employs a content-based filtering approach, where the system matches patient profiles with drug characteristics to recommend the most appropriate medications. This system aims to help and find the best suitable Medicine advise according to the diseases by applying library database. By combining machine learning techniques with a dedicated Health Advisor website, this research aims to enhance the personalized management of medicines