CLINICAL DECISION SUPPORT SYSTEM ON COPD PREDICTION USING BIG DATA ANALYTICS WITH IMPROVED PATIENT MATCHING
Main Article Content
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.
Downloads
Metrics
Article Details
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
