An Empirical Model of Supervised Learning for Electronic Health Records

Abstract

Examining the health records is an interesting research issue in the field of medical knowledge and data engineering. Electronic health records are basic sources which maintains the patient health information that contains vitals, demographics and encounter or episode information. We propose an empirical model of classification approach for analyzing the test samples with training samples of electronic health records. We use improved supervised learning model to classify the health records. Our proposed model gives efficient results than traditional approaches.