Predictors of Medication Adherence in Elderly Patients with Chronic Diseases Using Support Vector Machine Models.
- Author:
Soo Kyoung LEE
1
;
Bo Yeong KANG
;
Hong Gee KIM
;
Youn Jung SON
Author Information
- Publication Type:Original Article
- Keywords: Medication Adherence; Aged; Chronic Disease; Regression Analysis; Support Vector Machines
- MeSH: Aged; Chronic Disease; Depression; Health Literacy; Humans; Logistic Models; Medication Adherence; Regression Analysis; Support Vector Machine; Tertiary Care Centers
- From:Healthcare Informatics Research 2013;19(1):33-41
- CountryRepublic of Korea
- Language:English
- Abstract: OBJECTIVES: The aim of this study was to establish a prediction model of medication adherence in elderly patients with chronic diseases and to identify variables showing the highest classification accuracy of medication adherence in elderly patients with chronic diseases using support vector machine (SVM) and conventional statistical methods, such as logistic regression (LR). METHODS: We included 293 chronic disease patients older than 65 years treated at one tertiary hospital. For the medication adherence, Morisky's self-report was used. Data were collected through face-to-face interviews. The mean age of the patients was 73.8 years. The classification process was performed with LR (SPSS ver. 20.0) and SVM (MATLAB ver. 7.12) method. RESULTS: Taking into account 16 variables as predictors, the result of applying LR and SVM classification accuracy was 71.1% and 97.3%, respectively. We listed the top nine variables selected by SVM, and the accuracy using a single variable, self-efficacy, was 72.4%. The results suggest that self-efficacy is a key factor to medication adherence among a Korean elderly population both in LR and SVM. CONCLUSIONS: Medication non-adherence was strongly associated with self-efficacy. Also, modifiable factors such as depression, health literacy, and medication knowledge associated with medication non-adherence were identified. Since SVM builds an optimal classifier to minimize empirical classification errors in discriminating between patient samples, it could achieve a higher accuracy with the smaller number of variables than the number of variables used in LR. Further applications of our approach in areas of complex diseases, treatment will provide uncharted potentials to researchers in the domains.
