Condition   Expression Quick Search
When you enter More than two words, please use 'and , or' operation by means of putting ',(Comma Mark)' between each word.
19(01) 33-41
Predictors of Medication Adherence in Elderly Patients with Chronic Diseases Using Support Vector Machine Models
Soo Kyoung Lee, RN, PhD1, Bo-Yeong Kang, PhD2, Hong-Gee Kim, PhD1, Youn-Jung Son, RN, PhD3
1Biomedical Knowledge Engineering Lab., Seoul National University, Seoul; 2School of Mechanical Engineering, Kyungpook National University, Daegu; 3Department of Nursing, Soonchunhyang University, Cheonan, Korea
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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.
Healthcare Informatics Research 2013 Mar; 19(01) 33-41
Keyword : Medication Adherence, Aged, Chronic Disease, Regression Analysis, Support Vector Machines

Copyright © 2016 The Korean Society of Medical Informatics. All Rights Reserved.
1618 Kyungheegung Achim Bldg 3, 34 Sajik-ro 8-gil, Jongno-gu, Seoul 03174, Korea
Tel: +82-2-734-7637    Fax: +82-2-734-7763    E-mail:    Powered by, Ltd