Predictors of Medication Adherence in Elderly Patients with Chronic Diseases Using Support Vector Machine Models
ObjectivesThe 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 (...
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| Format: | Article |
| Language: | English |
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The Korean Society of Medical Informatics
2013-03-01
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| Series: | Healthcare Informatics Research |
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| Online Access: | http://e-hir.org/upload/pdf/hir-19-33.pdf |
| _version_ | 1819003709238018048 |
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| author | Soo Kyoung Lee Bo-Yeong Kang Hong-Gee Kim Youn-Jung Son |
| author_facet | Soo Kyoung Lee Bo-Yeong Kang Hong-Gee Kim Youn-Jung Son |
| author_sort | Soo Kyoung Lee |
| collection | DOAJ |
| description | ObjectivesThe 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).MethodsWe 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.ResultsTaking 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.ConclusionsMedication 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. |
| first_indexed | 2024-12-20T23:25:18Z |
| format | Article |
| id | doaj.art-d576b678f13748dba222c7a87e84f379 |
| institution | Directory Open Access Journal |
| issn | 2093-3681 2093-369X |
| language | English |
| last_indexed | 2024-12-20T23:25:18Z |
| publishDate | 2013-03-01 |
| publisher | The Korean Society of Medical Informatics |
| record_format | Article |
| series | Healthcare Informatics Research |
| spelling | doaj.art-d576b678f13748dba222c7a87e84f3792022-12-21T19:23:25ZengThe Korean Society of Medical InformaticsHealthcare Informatics Research2093-36812093-369X2013-03-01191334110.4258/hir.2013.19.1.33714Predictors of Medication Adherence in Elderly Patients with Chronic Diseases Using Support Vector Machine ModelsSoo Kyoung Lee0Bo-Yeong Kang1Hong-Gee Kim2Youn-Jung Son3Biomedical Knowledge Engineering Lab., Seoul National University, Seoul, Korea.School of Mechanical Engineering, Kyungpook National University, Daegu, Korea.Biomedical Knowledge Engineering Lab., Seoul National University, Seoul, Korea.Department of Nursing, Soonchunhyang University, Cheonan, Korea.ObjectivesThe 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).MethodsWe 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.ResultsTaking 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.ConclusionsMedication 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.http://e-hir.org/upload/pdf/hir-19-33.pdfmedication adherenceagedchronic diseaseregression analysissupport vector machines |
| spellingShingle | Soo Kyoung Lee Bo-Yeong Kang Hong-Gee Kim Youn-Jung Son Predictors of Medication Adherence in Elderly Patients with Chronic Diseases Using Support Vector Machine Models Healthcare Informatics Research medication adherence aged chronic disease regression analysis support vector machines |
| title | Predictors of Medication Adherence in Elderly Patients with Chronic Diseases Using Support Vector Machine Models |
| title_full | Predictors of Medication Adherence in Elderly Patients with Chronic Diseases Using Support Vector Machine Models |
| title_fullStr | Predictors of Medication Adherence in Elderly Patients with Chronic Diseases Using Support Vector Machine Models |
| title_full_unstemmed | Predictors of Medication Adherence in Elderly Patients with Chronic Diseases Using Support Vector Machine Models |
| title_short | Predictors of Medication Adherence in Elderly Patients with Chronic Diseases Using Support Vector Machine Models |
| title_sort | predictors of medication adherence in elderly patients with chronic diseases using support vector machine models |
| topic | medication adherence aged chronic disease regression analysis support vector machines |
| url | http://e-hir.org/upload/pdf/hir-19-33.pdf |
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