Predictors of Medication Adherence in Elderly Patients with Chronic Diseases Using Support Vector Machine Models

Lee, Soo Kyoung; Kang, Bo‐Yeong; Kim, Hong-Gee; Son, Youn‐Jung

doi:10.4258/hir.2013.19.1.33

Cited by 42 publications

(38 citation statements)

References 23 publications

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“…In another study Lee et al [12] compared SVM and LR for the classification of chronic disease and showed that SVM achieved higher accuracy with a smaller number of variables than the number of variables used in LR (71.1% for LR and 97.3% for SVM), which is consistent with our results.…”

Section: Discussionsupporting

confidence: 91%

“…Son et al [11] compared various kernel functions in the SVM technique for predicting medication adherence in heart failure patients. In another study conducted by Lee et al [12], the performance of SVM was evaluated and compared with LR for the classification of chronic disease. In a study conducted by Lehmann et al [13], the performance of four classification methods (RF, SVM, NN, and LDA) were compared for recognition of Alzheimer disease.…”

Section: Introductionmentioning

confidence: 99%

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Real-Data Comparison of Data Mining Methods in Prediction of Diabetes in Iran

Tapak

Mahjub

Hamidi³

et al. 2013

Healthc Inform Res

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ObjectivesDiabetes is one of the most common non-communicable diseases in developing countries. Early screening and diagnosis play an important role in effective prevention strategies. This study compared two traditional classification methods (logistic regression and Fisher linear discriminant analysis) and four machine-learning classifiers (neural networks, support vector machines, fuzzy c-mean, and random forests) to classify persons with and without diabetes.MethodsThe data set used in this study included 6,500 subjects from the Iranian national non-communicable diseases risk factors surveillance obtained through a cross-sectional survey. The obtained sample was based on cluster sampling of the Iran population which was conducted in 2005-2009 to assess the prevalence of major non-communicable disease risk factors. Ten risk factors that are commonly associated with diabetes were selected to compare the performance of six classifiers in terms of sensitivity, specificity, total accuracy, and area under the receiver operating characteristic (ROC) curve criteria.ResultsSupport vector machines showed the highest total accuracy (0.986) as well as area under the ROC (0.979). Also, this method showed high specificity (1.000) and sensitivity (0.820). All other methods produced total accuracy of more than 85%, but for all methods, the sensitivity values were very low (less than 0.350).ConclusionsThe results of this study indicate that, in terms of sensitivity, specificity, and overall classification accuracy, the support vector machine model ranks first among all the classifiers tested in the prediction of diabetes. Therefore, this approach is a promising classifier for predicting diabetes, and it should be further investigated for the prediction of other diseases.

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Section: Discussionsupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Real-Data Comparison of Data Mining Methods in Prediction of Diabetes in Iran

Tapak

Mahjub

Hamidi³

et al. 2013

Healthc Inform Res

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“…16,17 Earlier studies reported that the rate of cost-related medication nonadherence among older adults was 5.4% in 2004 and dropped to 3.6% in 2006, likely representing the impact of the passage of the Medicare Modernization Act in 2003, which led to the full implementation of Medicare Part D in 2006. 15,18 Furthermore, while there are many studies reporting the prevalence of and factors associated with medication nonadherence, they are often outdated, [19][20][21] exclude older adults, 22,23 have limited generalizability, 21,24 or do not elicit cost-specific medication nonadherence and its reasons among older adults. 25,26 In comparison to younger individuals, older adults have an increased risk of medication nonadherence because of multiple chronic conditions and higher medication use.…”

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confidence: 99%

Cost‐Related Medication Nonadherence Among Older Adults: Findings From a Nationally Representative Sample

Chung

Marottoli

Cooney

et al. 2019

J American Geriatrics Society

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OBJECTIVES To estimate the rate of and risk factors associated with cost‐related medication nonadherence among older adults. DESIGN Cross‐sectional analysis of the 2017 National Health Interview Survey (NHIS). SETTING Nationally representative health interview survey in the United States. PARTICIPANTS Survey respondents, aged 65 years or older (n = 5701 unweighted) in the 2017 wave of the NHIS. MEASUREMENTS Self‐reported, cost‐related medication nonadherence (due to cost: skip dose, reduce dose, or delay or not fill a prescription) and actions taken due to cost‐related medication nonadherence (ask for lower‐cost prescription, use alternative therapy, or buy medications from another country) were quantified. We used a series of multivariable logistic regression analyses to identify factors associated with cost‐related medication nonadherence. We also reported analyses by chronic disease subgroups. RESULTS In 2017, 408 (6.8%) of 5901 older adults, representative of 2.7 million older adults nationally, reported cost‐related medication nonadherence. Among those with cost‐related medication nonadherence, 44.2% asked a physician for lower‐cost medications, 11.5% used alternative therapies, and 5.3% bought prescription drugs outside the United States to save money. Correlates independently associated with a higher likelihood of cost‐related medication nonadherence included: younger age, female sex, lower socioeconomic levels (eg, low income and uninsured), mental distress, functional limitations, multimorbidities, and obesity (P < .05 for all). Similar patterns were found in subgroup analyses. CONCLUSION Cost‐related medication nonadherence among older adults is increasingly common, with several potentially modifiable risk factors identified. Interventions, such as medication therapy management, may be needed to reduce cost‐related medication nonadherence in older adults. J Am Geriatr Soc 67:2463–2473, 2019

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“…A study conducted in Nigeria also found that age, gender and education level did not impact medication adherence but the employment was a significant variable (Okuboyejo, 2014). Meanwhile a few studies have concluded that age and education level were significantly related to medication adherence in elderly patients with chronic disease and female patients were at increased risk for nonadherence to statins therapy (Lee et al, 2013;Lewey et al, 2013).…”

Section: Discussionmentioning

confidence: 99%

Impact of Mood Disorder on Medication Adherence in Patients with Chronic Diseases at a Shanghai Rural Hospital

Xia¹,

Xiao²,

Ma³

et al. 2015

International J. of Pharmacology

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A B S T R A C T Few studies were conducted on risk factor in medication nonadherence in developing rural area in China. The goal of this study was to perform a survey to address the prevalence of medication nonadherence and the reasons for nonadherence. Outpatients with chronic diseases (chronic obstructive pulmonary disease, diabetes, hyperlipidemia, hypertension, etc) in a rural hospital far away from Shanghai downtown, from Jan 1 to June 30, 2014 were invited to participate in this survey. Patients were answered a combination questionnaire including self-reported medication adherence and depression/anxiety symptoms. A total of 325 patients with chronic diseases were treated with regular medication without any anti-anxiety drugs or antidepressants. The 255 (78.5%) patients were regarded as medication nonadherence. Age, gender, educational level, brand preference and health literacy were not associated with medication adherence, while the number of chronic diseases, anxiety and depression were negatively impact medication adherence. Depression and anxiety symptoms are possible risk factors causing the nonadherence.

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Predictors of Medication Adherence in Elderly Patients with Chronic Diseases Using Support Vector Machine Models

Cited by 42 publications

References 23 publications

Real-Data Comparison of Data Mining Methods in Prediction of Diabetes in Iran

Real-Data Comparison of Data Mining Methods in Prediction of Diabetes in Iran

Cost‐Related Medication Nonadherence Among Older Adults: Findings From a Nationally Representative Sample

Impact of Mood Disorder on Medication Adherence in Patients with Chronic Diseases at a Shanghai Rural Hospital

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