Publication Type : Conference Paper
Publisher : 2016 Second International Conference on Cognitive Computing and Information Processing (CCIP)
Source : 2016 Second International Conference on Cognitive Computing and Information Processing (CCIP) (2016)
Url : http://ieeexplore.ieee.org/abstract/document/7802881/
ISBN : 9781509010257
Accession Number : 16579136
Keywords : Association rule analysis, Association rules, cardiovascular disease, Cardiovascular Diseases, Cardiovascular system, circulatory system, data analysis, Data mining, Diseases, Health care, Healthcare, Heart, heart disease, heart disease dataset analysis, patient specific data analysis, Statistical analysis, statistical validation, Testing, Training, UCI repository
Campus : Bengaluru
School : Department of Computer Science and Engineering, School of Engineering
Department : Computer Science, Mathematics
Year : 2016
Abstract : Data mining in healthcare is a rising field due to the vast amount of patient specific data which is freely available for analysis. While the majority of this data has been analyzed using various data mining techniques like classification, but association rule mining in this field is still largely unexplored. Association Rule Mining is a simple yet powerful tool that brings to light hidden relationships among data attributes in addition to statistically validating those which are already known. These relationships can help in understanding diseases and their causes in a better way, which in turn will help to prevent them. This report presents exploration of this field and the conclusions drawn from analyzing heart disease dataset from UCI repository. In this paper association rule mining is applied to cardiovascular disease. Cardiovascular diseases are diseases related to heart and circulatory system. Heart disease is explored in this paper.
Cite this Research Publication : S. Khare and Dr. Deepa Gupta, “Association rule analysis in cardiovascular disease”, in 2016 Second International Conference on Cognitive Computing and Information Processing (CCIP), 2016.