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KMID : 0603720100160040253
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Journal of Korean Society of Medical Informatics
2010 Volume.16 No. 4 p.253 ~ p.259
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Application of Support Vector Machine for Predic-tion of Medication Adherence in Heart Failure Pa-tients
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Son Youn-Jung
Kim Hong-Gee
Kim Eung-Hee
Choi Sang-Sup
Lee Soo-Kyoung
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Abstract
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Objectives: Heart failure (HF) is a progressive syndrome that marks the end-stage of heart diseases, and it has a high mortal-ity rate and significant cost burden. In particular, non-adherence of medication in HF patients may result in serious conse-quences such as hospital readmission and death. This study aims to identify predictors of medication adherence in HF pa-tients. In this work, we applied a Support Vector Machine (SVM), a machine-learning method useful for data classification.
Methods: Data about medication adherence were collected from patients at a university hospital through self-reported ques-tionnaire. The data included 11 variables of 76 patients with HF. Mathematical simulations were conducted in order to de-velop a SVM model for the identification of variables that would best predict medication adherence. To evaluate the robust-ness of the estimates made with the SVM models, leave-one-out cross-validation (LOOCV) was conducted on the data set.
Results: The two models that best classified medication adherence in the HF patients were: one with five predictors (gender, daily frequency of medication, medication knowledge, New York Heart Association [NYHA] functional class, spouse) and the other with seven predictors (age, education, monthly income, ejection fraction, Mini-Mental Status Examination-Korean [MMSE-K], medication knowledge, NYHA functional class). The highest detection accuracy was 77.63%.
Conclusions: SVM modeling is a promising classification approach for predicting medication adherence in HF patients. This predictive model helps stratify the patients so that evidence-based decisions can be made and patients managed appropriately. Further, this ap-proach should be further explored in other complex diseases using other common variables.
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KEYWORD
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Heart Failure, Medication, Patient Adherence, Support Vector Machine
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