Journal
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
Volume 17, Issue 3, Pages 881-891Publisher
SPRINGER
DOI: 10.1007/s10586-013-0308-1
Keywords
Coronary heart disease; Data mining; Fuzzy logic; Decision tree; FbACHD_PSM
Funding
- MSIP (the Ministry of Science, ICT and Future Planning), Korea, under the IT-CRSP (IT Convergence Research Support Program) [NIPA-2013-H0401-13-1001]
- Industrial Strategic technology development program, Ministry of Trade, industry & Energy (MI, Korea) [10037283]
- Korea Evaluation Institute of Industrial Technology (KEIT) [10037283] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
- National Research Foundation of Korea [22A20130012228] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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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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