Proposed Methodology for Personalized Medicine in Healthcare System using Data Mining Techniques with an Emphasis on Mental Illness Disease

Abstract

Today, the Medicare system has been improved their models to achieve the best care for the patients and customized the delivery of healthcare treatment to the individuals by helping the doctors in the diagnosis of a disease. The systems are useful due to implementation of personalized medicine that is an effective way of doing treatment to a patient by considering omic data and electronic health record data while classifying the patient into subgroups based on common biological basis of diseases. This paper discusses the mental illness disease and its categorization to predict a disease based on its symptoms along with various challenges while processing data sets. There is data mining and machine learning algorithms used for feature selection and classification in order to improve the prediction performance for the specific mental illness disease are discussed to develop a new model. The proposed methodology is to develop a personalized Medicare system considering omic or Electronic Health Record (EHR) data using the best feature selection and classification mining algorithms to help the doctors to diagnose the disease at the early stage and identify the root cause for better treatment.