Adaptive mining prediction model for content recommendation to coronary heart disease patients
- Authors
- Kim, Jae-Kwon; Lee, Jong-Sik; Park, Dong-Kyun; Lim, Yong-Soo; Lee, Young-Ho; Jung, Eun-Young
- Issue Date
- Sep-2014
- Publisher
- SPRINGER
- Keywords
- Coronary heart disease; Data mining; Fuzzy logic; Decision tree; FbACHD_PSM
- Citation
- CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, v.17, no.3, pp.881 - 891
- Journal Title
- CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
- Volume
- 17
- Number
- 3
- Start Page
- 881
- End Page
- 891
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/12338
- DOI
- 10.1007/s10586-013-0308-1
- ISSN
- 1386-7857
- Abstract
- This paper proposes the Fuzzy Rule-based Adaptive Coronary Heart Disease Prediction Support Model (FbACHD_PSM), which gives content recommendation to coronary heart disease patients. The proposed model uses a mining technique validated by medical experts to provide recommendations. FbACHD_PSM consists of three parts for heart disease risk prediction. First, a fuzzy membership function is constructed using medical guidelines and statistical methods. Then, a decision-tree rule induction technique creates mining-based rules that are subjected to validation by medical experts. As the rules may not be medically suitable, the experts add rules that have been verified and delete inappropriate rules. Thirdly, using fuzzy inference based on Mamdani's method, the model predicts the risk of heart disease. Based on this, final recommendations are provided to patients regarding normal living, nutrition control, exercise, and drugs. To implement our proposed model and evaluate its performance, we use a dataset from a single tertiary hospital.
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Collections - IT융합대학 > 컴퓨터공학과 > 1. Journal Articles
- 의과대학 > 의학과 > 1. Journal Articles
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