A Machine Learning Model for Disease Prediction and Remote Patient Monitoring

People's health reports, including diagnostic information and medical prescriptions, are delivered in the form of test-based case notes, making it impossible to determine a person's prior health issues or medications taken until he or she returns to the hospital later on. Storing all of a person's health information in the cloud as a soft copy, on the other hand, alleviates this issue. To accomplish this, each and every hospital, dispensary, and laboratory must have an internet connection for the registration of patient data. A unique Health Id will identify each patient, and all of the patient's data will be stored in the cloud, where the specific patient can only access it. In order to prevent and treat illness, it is critical to perform an accurate and timely analysis on any health-related problem. The ability to diagnose disease by obtaining all information from a linked Health ID combined with Machine Learning techniques will improve the system's ability to detect diseases. We believe that our diagnostic model can operate like a doctor in the earlier diagnosis of this disease, allowing for timely treatment and the preservation of life.