Data Modeling Using Vital Sign Dynamics for In-hospital Mortality Classification in Patients with Acute Coronary Syndrome.

IF 2.3 Q3 MEDICAL INFORMATICS Healthcare Informatics Research Pub Date : 2023-04-01 DOI:10.4258/hir.2023.29.2.120
Sarawuth Limprasert, Ajchara Phu-Ang
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Abstract

Objectives: This study compared feature selection by machine learning or expert recommendation in the performance of classification models for in-hospital mortality among patients with acute coronary syndrome (ACS) who underwent percutaneous coronary intervention (PCI).

Methods: A dataset of 1,123 patients with ACS who underwent PCI was analyzed. After assigning 80% of instances to the training set through random splitting, we performed feature scaling and resampling with the synthetic minority over-sampling technique and Tomek link method. We compared two feature selection.

Methods: recursive feature elimination with cross-validation (RFECV) and selection by interventional cardiologists. We used five simple models: support vector machine (SVM), random forest, decision tree, logistic regression, and artificial neural network. The performance metrics were accuracy, recall, and the false-negative rate, measured with 10-fold cross-validation in the training set and validated in the test set.

Results: Patients' mean age was 66.22 ± 12.88 years, and 33.63% had ST-elevation ACS. Fifteen of 34 features were selected as important with the RFECV method, while the experts chose 11 features. All models with feature selection by RFECV had higher accuracy than the models with expert-chosen features. In the training set, the random forest model had the highest accuracy (0.96 ± 0.01) and recall (0.97 ± 0.02). After validation in the test set, the SVM model displayed the highest accuracy (0.81) and a recall of 0.61.

Conclusions: Models with feature selection by RFECV had higher accuracy than those with feature selection by experts in identifying patients with ACS at high risk for in-hospital mortality.

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急性冠脉综合征患者住院死亡率分类的生命体征动力学数据建模。
目的:本研究比较了机器学习特征选择和专家推荐对急性冠脉综合征(ACS)患者经皮冠状动脉介入治疗(PCI)住院死亡率分类模型的性能。方法:对1123例行PCI治疗的ACS患者数据集进行分析。通过随机分割将80%的实例分配到训练集后,我们使用合成少数派过采样技术和Tomek链接方法进行特征缩放和重采样。我们比较了两种特征选择。方法:交叉验证递归特征消除(RFECV)和介入心脏病专家选择。我们使用了五个简单的模型:支持向量机(SVM)、随机森林、决策树、逻辑回归和人工神经网络。性能指标是准确性、召回率和假阴性率,在训练集中进行10倍交叉验证,并在测试集中进行验证。结果:患者平均年龄66.22±12.88岁,其中33.63%为st段抬高型ACS。用RFECV方法从34个特征中选择了15个作为重要特征,而专家选择了11个特征。RFECV特征选择模型的准确率均高于专家特征选择模型。在训练集中,随机森林模型具有最高的准确率(0.96±0.01)和召回率(0.97±0.02)。经过测试集的验证,SVM模型的准确率最高(0.81),召回率为0.61。结论:RFECV特征选择模型识别院内死亡高危ACS患者的准确率高于专家特征选择模型。
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来源期刊
Healthcare Informatics Research
Healthcare Informatics Research MEDICAL INFORMATICS-
CiteScore
4.90
自引率
6.90%
发文量
44
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