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首页> 外文期刊>Journal of healthcare engineering. >Neural Network-Based Coronary Heart Disease Risk Prediction Using Feature Correlation Analysis
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Neural Network-Based Coronary Heart Disease Risk Prediction Using Feature Correlation Analysis

机译：基于神经网络的冠心病风险预测使用特征相关分析

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Background. Of the machine learning techniques used in predicting coronary heart disease (CHD), neural network (NN) is popularly used to improve performance accuracy. Objective. Even though NN-based systems provide meaningful results based on clinical experiments, medical experts are not satisfied with their predictive performances because NN is trained in a “black-box” style. Method. We sought to devise an NN-based prediction of CHD risk using feature correlation analysis (NN-FCA) using two stages. First, the feature selection stage, which makes features acceding to the importance in predicting CHD risk, is ranked, and second, the feature correlation analysis stage, during which one learns about the existence of correlations between feature relations and the data of each NN predictor output, is determined. Result. Of the 4146 individuals in the Korean dataset evaluated, 3031 had low CHD risk and 1115 had CHD high risk. The area under the receiver operating characteristic (ROC) curve of the proposed model (0.749?±?0.010) was larger than the Framingham risk score (FRS) (0.393?±?0.010). Conclusions. The proposed NN-FCA, which utilizes feature correlation analysis, was found to be better than FRS in terms of CHD risk prediction. Furthermore, the proposed model resulted in a larger ROC curve and more accurate predictions of CHD risk in the Korean population than the FRS.
机译：背景。在预测冠心病（CHD）的机器学习技术中，神经网络（NN）普遍用于提高性能准确性。客观的。尽管基于NN的系统根据临床实验提供了有意义的结果，但医学专家对其预测性表现不满意，因为NN在“黑匣子”风格中培训。方法。我们试图使用两个阶段使用特征相关性分析（NN-FCA）设计基于NN的CHD风险预测。首先，使特征选择阶段使得通过预测CHD风险的重要性，排名和第二特征相关分析阶段，在此期间，在此期间学习特征关系与每个NN预测器的数据之间的相关性存在的存在输出，确定。结果。在评估的韩国数据集中的4146个个体中，3031年的CHD风险低，1115次高风险。所提出的模型的接收器操作特性（ROC）曲线下的区域（0.749？±0.010）大于Framingham风险评分（FRS）（0.393？±0.010）。结论。在CHD风险预测方面，发现所提出的NN-FCA利用特征相关分析，比FRS更好。此外，所提出的模型导致较大的ROC曲线和更准确的预测韩国人群中的CHD风险的预测比FRS。

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