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Adaptive mining prediction model for content recommendation to coronary heart disease patients

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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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Acknowledgements

This research was supported by MSIP (the Ministry of Science, ICT and Future Planning), Korea, under the IT-CRSP (IT Convergence Research Support Program) (NIPA-2013-H0401-13-1001) supervised by the NIPA (National IT Industry Promotion Agency).

This work was supported by the Industrial Strategic technology development program, 10037283, funded By the Ministry of Trade, industry & Energy (MI, Korea).

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Inha University, Incheon, Korea

    Jae-Kwon Kim & Jong-Sik Lee

  2. u-Healthcare Center, Gachon University Gil Hospital, Incheon, Korea

    Dong-Kyun Park, Yong-Soo Lim & Eun-Young Jung

  3. School of Information Technology, Gachon University, Seongnam, Korea

    Young-Ho Lee

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  1. Jae-Kwon Kim
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Correspondence to Eun-Young Jung.

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Kim, JK., Lee, JS., Park, DK. et al. Adaptive mining prediction model for content recommendation to coronary heart disease patients. Cluster Comput 17, 881–891 (2014). https://doi.org/10.1007/s10586-013-0308-1

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