Healthcare studies prove that heart disease has increased in recent decades and the growth of patients suffering from heart problems does not stop. In this regard, various data mining techniques have been used by machine learning researchers to support health professionals in the decision-making of this disease. Many of these techniques are based on basic machine learning classifiers, others integrate these classifiers in streaming systems in order to accelerate the execution time. However, some heart situations demand early detection to reduce the chance of having a dangerous illness and the existing machine learning solutions are not appropriate for real-time analysis, because we need to accelerate the algorithms themselves. In this paper, an online algorithm called Enhanced Hoeffding Anytime Tree (EHATT) is proposed to efficiently predict heart disease. EHATT is based on Hoeffding Anytime Tree (HATT), the last version of incremental decision trees. The amelioration that was made by EHATT on HATT, is the change of the node splitting evaluation function with another more suitable for split measures. To examine the performance of EHATT, four metrics are evaluated: classification accuracy, time, memory, and tree size. The experiment results show that EHATT achieves good performance to predict heart disease.

中文翻译：

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增强型 Hoeffding Anytime Tree：一种早期预测心脏病的实时算法

医疗保健研究证明，近几十年来心脏病有所增加，心脏病患者的增长并没有停止。在这方面，机器学习研究人员已使用各种数据挖掘技术来支持卫生专业人员对这种疾病的决策。其中许多技术基于基本的机器学习分类器，其他技术将这些分类器集成到流系统中以加快执行时间。然而，一些心脏情况需要及早发现以减少患上危险疾病的机会，而现有的机器学习解决方案不适合实时分析，因为我们需要对算法本身进行加速。在本文中，提出了一种称为增强型 Hoeffding Anytime Tree (EHATT) 的在线算法来有效地预测心脏病。EHATT 基于增量决策树的最新版本 Hoeffding Anytime Tree (HATT)。EHATT对HATT所做的改进，是节点分裂评价函数的变化，换成了另一种更适合分裂的措施。为了检查 EHATT 的性能，评估了四个指标：分类准确度、时间、内存和树大小。实验结果表明，EHATT在预测心脏病方面取得了良好的效果。为了检查 EHATT 的性能，评估了四个指标：分类准确度、时间、内存和树大小。实验结果表明，EHATT在预测心脏病方面取得了良好的效果。为了检查 EHATT 的性能，评估了四个指标：分类准确度、时间、内存和树大小。实验结果表明，EHATT在预测心脏病方面取得了良好的效果。