Application of Support Vector Machine for Prediction of Medication Adherence in Heart Failure Patients

By Y. Son, Hong-Gee Kim, Eung-Hee Kim, Sangsup Choi, Soo-Kyoung Lee. 2010

174 citations recorded by the source corpus.

Objectives Heart failure (HF) is a progressive syndrome that marks the end-stage of heart diseases, and it has a high mortality rate and significant cost burden. In particular, non-adherence of medication in HF patients may result in serious consequences such as hospital readmission and death. This study aims to identify predictors of medication adherence in HF patients. 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 questionnaire. The data included 11 variables of 76 patients with HF. Mathematical simulations were conducted in order to develop a SVM model for the identification of variables that would best predict medication adherence. To evaluate the robustness 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 approach should be further explored in other complex diseases using other common variables.

Subfields: Cardiology and Cardiovascular Medicine, Family Practice

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Citations in the corpus

Federated learning of predictive models from federated Electronic Health Records (2018, background): “…Vector Machines; the applications range from prediction of the onset of diabetes,[1,2] prediction of hospitalizations for cardiac events,[3] prediction of medication adherence in heart failure patients,[4] and cancer diagnosis,[5] to automated recognition of the obstructive sleep apnea syndrome .”
Data mining in clinical big data: the frequently used databases, steps, and methodological models (2021, methodology): “Additionally, the mechanisms of certain chronic and complex diseases observed in clinical practice remain unclear, and many risk factors, including gene–gene interactions and gene-environment interactions, must be considered in the research of such diseases [55, 56].”