Heart disease prediction is a critical task regarding human health. It is based on deriving an Machine Learning model from medical parameters to predict risk levels. In this work, we propose and test novel ensemble methods for heart disease prediction. Randomness analysis of distance sequences is utilized to derive a classifier, which is served as a base estimator of a bagging scheme. Method is successfully tested on medical Spectf dataset. Additionally, a Graph Lasso and Ledoit–Wolf shrinkage-based classifier is developed for Statlog dataset which is a UCI data. These two algorithms yield comparatively good accuracy results: 88.7 and 88.8 for Spectf and Statlog, respectively. These proposed algorithms provide promising results and novel classification methods that can be utilized in various domains to improve performance of ensemble methods.

心脏病预测是有关人类健康的关键任务。它基于从医学参数得出机器学习模型来预测风险水平。在这项工作中，我们提出并测试了用于预测心脏病的新型集成方法。利用距离序列的随机性分析得出分类器，该分类器用作装袋方案的基本估计器。方法已在医学Spectf数据集上成功测试。此外，还为Statlog数据集（它是UCI数据）开发了基于图套索和Ledoit-Wolf收缩的分类器。这两种算法产生了相对较好的精度结果：Spectf和Statlog分别为88.7和88.8。这些提出的算法提供了有希望的结果和新颖的分类方法，可以在各个领域中使用它们来改善集成方法的性能。