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首页> 外文会议>IEEE International Conference on Bioinformatics and Computational Biology >Using Machine Learning Models to Study Medication Adherence in Hypertensive Patients Based on National Stroke Screening Data
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Using Machine Learning Models to Study Medication Adherence in Hypertensive Patients Based on National Stroke Screening Data

机译：采用机器学习模型在基于国家中风筛查数据的高血压患者中研究药物粘附

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摘要

Stroke, with high incidence, prevalence and mortality, has brought a heavy burden to families as well as society in China nowadays. In 2009, the China national stroke screening and intervention program was launched by the Ministry of Health of China. In the national program, risk factors of stroke are screened and people aged over 40 with high-risk of stroke will be followed-up. From the experience, it is found that hypertension is an important risk factor of stroke. Improving the adherence of hypertension medication can effectively control blood pressure and further decrease stroke incidence. In this study, firstly, we employ oversampling and undersampling method to process the imbalanced dataset. Then, we build four machine learning models, namely logistic regression model, decision tree model, neural network model and random forest model, to classify medication adherence in hypertensive patients. We use the recall and precision to evaluate these models, and considering these two criteria, the model based on decision tree achieves best performance. The models constructed in this paper can be used to identify people with low adherence of antihypertensive drugs in the stroke screening program and improve the efficiency of the follow-up interventions, which can effectively control blood pressure and reduce the possibility of stroke.
机译：卒中发病率高，患病率和死亡率，对家庭以及中国的社会带来了沉重的负担。 2009年，中国国家中风筛查和干预计划由中国卫生部发起。在国家计划中，筛查中风的危险因素，并随访中风的高40岁以上的人。从经验中，发现高血压是中风的重要危险因素。改善高血压药物的粘附可以有效地控制血压并进一步降低行程入射。在本研究中，首先，我们采用过采样和欠采样方法来处理不平衡数据集。然后，我们建立四台机器学习模型，即Logistic回归模型，决策树模型，神经网络模型和随机林模型，对高血压患者进行药物依从性。我们使用召回和精确度来评估这些模型，并考虑到这两个标准，基于决策树的模型实现了最佳性能。本文构建的模型可用于识别中风筛查计划中抗高血压药物粘附性低的人，提高随访干预的效率，可有效控制血压并降低中风的可能性。

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