Kim Jae Kwon, Kang Sanggil
Department of Computer Engineering, Inha University, Incheon, Republic of Korea
J Healthc Eng. 2017;2017. doi: 10.1155/2017/2780501.
Of the machine learning techniques used in predicting coronary heart disease (CHD), neural network (NN) is popularly used to improve performance accuracy.
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.
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.
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).
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.
在用于预测冠心病（CHD）的机器学习技术中，神经网络（NN）被广泛用于提高预测准确性。
尽管基于神经网络的系统在临床实验中提供了有意义的结果，但医学专家对其预测性能并不满意，因为神经网络是以“黑箱”方式进行训练的。
我们试图分两个阶段使用特征相关分析（NN-FCA）来设计一种基于神经网络的冠心病风险预测方法。首先是特征选择阶段，对在预测冠心病风险中具有重要性的特征进行排序，其次是特征相关分析阶段，在此阶段确定特征关系与每个神经网络预测器输出数据之间相关性的存在情况。
在评估的韩国数据集中的4146名个体中，3031人冠心病风险较低，1115人冠心病风险较高。所提出模型的受试者操作特征（ROC）曲线下面积（0.749±0.010）大于弗雷明汉风险评分（FRS）（0.393±0.010）。
所提出的利用特征相关分析的NN-FCA在冠心病风险预测方面优于FRS。此外，与FRS相比，所提出的模型在韩国人群中产生了更大的ROC曲线和更准确的冠心病风险预测。
