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Investigation of chronic disease correlation using data mining techniques

Publication Type : Conference Paper

Publisher : 2015 2nd International Conference on Recent Advances in Engineering and Computational Sciences, RAECS 2015

Source : 2015 2nd International Conference on Recent Advances in Engineering and Computational Sciences, RAECS 2015, Institute of Electrical and Electronics Engineers Inc. (2015)

Url : https://www.scopus.com/inward/record.uri?eid=2-s2.0-84966267409&partnerID=40&md5=93c95cb539e2d145e7d4f3faec886c18

ISBN : 9781467382533

Keywords : Abnormal conditions, Cardiology, Cardiovascular system, chronic disease, Circulatory systems, Codes (symbols), Data mining, Diagnosis, Diseases, human anatomic system, ICD9 diagnostic codes, ischemic heart disease, Musculoskeletal system, Optimal sets, Respiratory system

Campus : Bengaluru

School : Department of Computer Science and Engineering, School of Engineering

Department : Community Medicine, Computer Science, Mathematics

Year : 2015

Abstract : A disease is an abnormal condition that affects the structure and function of one or more parts of the body. It may be caused by various factors, external and internal dysfunctions. There is a trend of various chronic diseases in any society. The major concern is that these chronic diseases are leading to many other diseases in future. An attempt to explore the correlation of various chronic diseases has become a necessity. This can be achieved by using data mining techniques, which help to derive knowledge about the affects of a particular chronic disease on the other chronic diseases. Since there is growing trend of diabetes and ischemic heart disease in the society, in this paper the focus is to investigate the effect of these diseases on the other chronic diseases using the ICD9 diagnostic codes. To achieve this goal various types of data mining techniques are used. The conclusion is an optimal set of ICD9 diagnostic codes associated with individuals having diabetes or ischemic heart disease. These codes are then investigated based on the human anatomic systems i.e. Circulatory system, Respiratory system, Nervous system, Musculoskeletal system, Renal system and Neoplasm and their relevance is justified. © 2015 IEEE.

Cite this Research Publication : Va Dominic, Dr. Deepa Gupta, Sangita Khare, and Aggarwal, Ab, “Investigation of chronic disease correlation using data mining techniques”, in 2015 2nd International Conference on Recent Advances in Engineering and Computational Sciences, RAECS 2015, 2015.

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