Application of support vector machine for prediction of medication adherence in heart failure patients.
Application of support vector machine for prediction of medication adherence in heart failure patients.
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DOI：
10.4258/hir.2010.16.4.253
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发表时间：
2010-12
影响因子：
2.9
通讯作者：
Lee SK
中科院分区：
文献类型：
--
作者：
Son YJ;Kim HG;Kim EH;Choi S;Lee SK
心力衰竭(HF)是一种进行性综合征，标志着心脏疾病的终末期，它具有高死亡率和巨大的成本负担。特别是，心力衰竭患者如果不坚持用药，可能会导致重新入院和死亡等严重后果。本研究旨在确定心衰患者服药依从性的预测因素。在这项工作中，我们应用了支持向量机(支持向量机)，这是一种用于数据分类的机器学习方法。用药依从性数据来自某大学医院患者的自填式问卷调查。数据包括76名心力衰竭患者的11个变量。进行了数学模拟，以开发一个支持向量机模型，用于识别最能预测药物依从性的变量。为了评估支持向量机模型估计的稳健性，对数据集进行了留一交叉验证(LOOCV)。对心力衰竭患者的服药依从性分类最好的两个模型是：一个有5个预测因素(性别、每日用药频率、用药知识、纽约心脏协会[NYHA]功能分级、配偶)，另一个有7个预测因素(年龄、教育程度、月收入、射血分数、简易智力状态检查-韩国[MMSE-K]、用药知识、NYHA功能分级)。最高检测准确率为77.63%。支持向量机模型是预测心力衰竭患者服药依从性的一种有前景的分类方法。这种预测模型有助于对患者进行分层，以便能够做出循证决策，并对患者进行适当的管理。此外，这一方法应该在使用其他常见变量的其他复杂疾病中进一步探索。
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. 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. 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%. 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.