College of Management and Economics, Tianjin University, Nankai District, Tianjin, 300072, People's Republic of China.
School of Mathematical Science, Hebei Normal University, Yuhua District, Shijiazhuang, 050024, People's Republic of China.
BMC Med Inform Decis Mak. 2021 Feb 25;21(1):73. doi: 10.1186/s12911-021-01436-7.
Heart disease is the primary cause of morbidity and mortality in the world. It includes numerous problems and symptoms. The diagnosis of heart disease is difficult because there are too many factors to analyze. What's more, the misclassification cost could be very high.
A cost-sensitive ensemble method was proposed to improve the efficiency of diagnosis and reduce the misclassification cost. The proposed method contains five heterogeneous classifiers: random forest, logistic regression, support vector machine, extreme learning machine and k-nearest neighbor. T-test was used to investigate if the performance of the ensemble was better than individual classifiers and the contribution of Relief algorithm.
The best performance was achieved by the proposed method according to ten-fold cross validation. The statistical tests demonstrated that the performance of the proposed ensemble was significantly superior to individual classifiers, and the efficiency of classification was distinctively improved by Relief algorithm.
The proposed ensemble gained significantly better results compared with individual classifiers and previous studies, which implies that it can be used as a promising alternative tool in medical decision making for heart disease diagnosis.
心脏病是世界上发病率和死亡率的主要原因。它包括许多问题和症状。由于需要分析的因素太多，心脏病的诊断比较困难。更重要的是，错误分类的代价可能非常高。
提出了一种基于代价敏感的集成方法，以提高诊断效率并降低错误分类的代价。所提出的方法包含五个异构分类器：随机森林、逻辑回归、支持向量机、极限学习机和 k-最近邻。使用 T 检验来研究集成的性能是否优于单个分类器和 Relief 算法的贡献。
根据十折交叉验证，所提出的方法取得了最佳性能。统计检验表明，所提出的集成的性能明显优于单个分类器，并且 Relief 算法显著提高了分类效率。
与单个分类器和以前的研究相比，所提出的集成方法取得了显著更好的结果，这意味着它可以作为心脏病诊断中医疗决策的一种很有前途的替代工具。
