CLINICAL DECISION SUPPORT SYSTEM ON COPD PREDICTION USING BIG DATA ANALYTICS WITH IMPROVED PATIENT MATCHING

Abstract

Big data analytics is a fast developing area that plays a vital part in research and health care practice advancements. Clinical Decision Support systems need patient identification and matching of their information residing in different systems for making better diagnosis and treatments at the right time. The COPD (Chronic Obstructive Pulmonary Disease) was a major cause of mortality and morbidity global outcome in social and financial burdens which was increasing significantly. In this paper Clinical Decision Support (CDS) system on COPD prediction with improved Patient matching utilizing big data analytics is presented. As the healthcare organizations share different documents of patients from different systems such as pharmacy, laboratory, claim systems, etc. they are required to be link with correct patient records for guarantying the better decisions by clinicians and minimized duplicate information and overheads. The Fuzzy Matching algorithm & Map Reduce are introduced in this work for addressing the issue of patient’s record matching from various systems to support clinical decision greatly. Then utilizing the data mining application of big data, Decision Tree (DT) model is applied to obtain best approach in the detection of COPD in independent patients. The result analysis show that this system has scalability and flexibility utilizing any fuzzy algorithm and handling the data source exhibits greater accuracy in COPD patient diagnosis with better efficiency.