论文标题
基于方差分析的自动属性选择和心脏病预后的预测模型
ANOVA-based Automatic Attribute Selection and a Predictive Model for Heart Disease Prognosis
论文作者
论文摘要
研究表明，心血管疾病（CVD）对人类健康是恶性的研究。因此，重要的是具有有效的CVD预后方法。为此，医疗保健行业采用了基于机器学习的智能解决方案，以减轻CVD预后的手动过程。因此，这项工作提出了一种信息融合技术，该技术通过分析方差（ANOVA）和域专家的知识结合了人的关键属性。它还引入了新的CVD数据样本集，用于新兴研究。进行了三十八个实验，以验证在四个公开可用的基准数据集中提出的框架的性能以及在这项工作中新创建的数据集。消融研究表明，所提出的方法可以达到竞争性平均平均准确性（MAA）为99.2％，平均AUC平均AUC为97.9％。
Studies show that Studies that cardiovascular diseases (CVDs) are malignant for human health. Thus, it is important to have an efficient way of CVD prognosis. In response to this, the healthcare industry has adopted machine learning-based smart solutions to alleviate the manual process of CVD prognosis. Thus, this work proposes an information fusion technique that combines key attributes of a person through analysis of variance (ANOVA) and domain experts' knowledge. It also introduces a new collection of CVD data samples for emerging research. There are thirty-eight experiments conducted exhaustively to verify the performance of the proposed framework on four publicly available benchmark datasets and the newly created dataset in this work. The ablation study shows that the proposed approach can achieve a competitive mean average accuracy (mAA) of 99.2% and a mean average AUC of 97.9%.
