Classification of Diagnostic Codes of Chronic Condition and Performance Evaluation of Various Approaches
Mohan Kumar K N1, S. Sampath2, Mohammed Imran3
1Mohan Kumar K N, Dept. of Computer Science Engineering., SJB Institute of Technology, Bangalore, India.
2S. Sampath, Dept. of ISE., Adichunchangiri Institute of Technology, City Chikkamagaluru, India.
3Mohammed Imran, Research and Development, Ejyle Technology, Bangalore, India.

Manuscript received on 06 April 2019 | Revised Manuscript received on 13 May 2019 | Manuscript published on 30 May 2019 | PP: 1072-1077 | Volume-8 Issue-1, May 2019 | Retrieval Number: A1301058119/19©BEIESP
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Abstract: Health in simple words is normal functioning of human body and disease is abnormal condition that affects normal functioning of human body without any external injury. Health care is all about the prevention of diseases by diagnosis and treatment. The majority of the population across world suffers from chronic disease which is a long term disease leading to multiple ailments if not taken care and chronic diseases cannot be prevented by vaccines or cured by medication, nor do they just disappear. Efforts are needed to build an efficient system which can predict, classify diseases and detect anomalies from health records. Electronic medical records are not better than the old manual records. This paper focuses on Medi-Claim data as it standout uniquely due to its authenticity, volume and demography attributes. Importantly HCC and ICD based coding are compatible with claim data set. This nature of ICD and HCC coding encouraged us to work with Medi-claim data set and HCC coding to build a Machine Learning model for preventive care of chronic diseases. The correlation between diabetes chronic disease and other chronic diseases is established through HCC codes using Machine Learning approaches. Effective inferences are drawn from the perspective of clinical relevance.
Index Terms: HCC, Chronic Condition, Diabetes, Ensemble.

Scope of the Article: Classification