Zaidi Syed Ali Jafar, Ghafoor Attia, Kim Jun, Abbas Zeeshan, Lee Seung Won
Institute of Information Technology, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, Pakistan.
Department of Metabiohealth, Sungkyunkwan University, Suwon 16419, Republic of Korea.
Healthcare (Basel). 2025 Feb 26;13(5):507. doi: 10.3390/healthcare13050507.
Cardiovascular disease (CVD) is a prominent determinant of mortality, accounting for 17 million lives lost across the globe each year. This underscores its severity as a critical health issue. Extensive research has been undertaken to refine the forecasting of CVD in patients using various supervised, unsupervised, and deep learning approaches.
This study presents HeartEnsembleNet, a novel hybrid ensemble learning model that integrates multiple machine learning (ML) classifiers for CVD risk assessment. The model is evaluated against six classical ML classifiers, including support vector machine (SVM), gradient boosting (GB), decision tree (DT), logistic regression (LR), k-nearest neighbor (KNN), and random forest (RF). Additionally, we compare HeartEnsembleNet with Hybrid Random Forest Linear Models (HRFLM) and ensemble techniques including stacking and voting.
Employing a dataset of 70,000 cardiac patients with 12 clinical attributes, our proposed model achieves a notable accuracy of 92.95% and a precision of 93.08%.
These results highlight the effectiveness of hybrid ensemble learning in enhancing CVD risk prediction, offering a promising framework for clinical decision support.
心血管疾病（CVD）是死亡率的一个主要决定因素，每年在全球导致1700万人死亡。这突出了其作为一个关键健康问题的严重性。已经进行了广泛的研究，以使用各种监督、无监督和深度学习方法来改进对患者心血管疾病的预测。
本研究提出了HeartEnsembleNet，这是一种新颖的混合集成学习模型，它集成了多个用于心血管疾病风险评估的机器学习（ML）分类器。该模型与六个经典的ML分类器进行了评估，包括支持向量机（SVM）、梯度提升（GB）、决策树（DT）、逻辑回归（LR）、k近邻（KNN）和随机森林（RF）。此外，我们将HeartEnsembleNet与混合随机森林线性模型（HRFLM）以及包括堆叠和投票在内的集成技术进行了比较。
使用一个包含70000名心脏病患者和12个临床属性的数据集，我们提出的模型实现了92.95%的显著准确率和93.08%的精确率。
这些结果突出了混合集成学习在增强心血管疾病风险预测方面的有效性，为临床决策支持提供了一个有前景的框架。
