Support Vector Machine (SVM) modeling achieved a maximum classification accuracy of 77.63% in predicting medication adherence among patients with heart failure.
Cross-Sectional (n=76)
No
Does Support Vector Machine modeling accurately predict medication adherence in heart failure patients?
Support Vector Machine modeling is a feasible approach for predicting medication adherence in heart failure patients, achieving up to 77.63% accuracy.
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.
Son et al. (Fri,) conducted a cross-sectional in Heart Failure (n=76). Support Vector Machine (SVM) modeling was evaluated on Accuracy of predicting medication adherence. Support Vector Machine (SVM) modeling achieved a maximum classification accuracy of 77.63% in predicting medication adherence among patients with heart failure.