A. B. Majumder, Somsubhra Gupta, Dharmpal Singh, Biwaranjan Acharya, V. Gerogiannis, Andreas Kanavos, P. Pintelas
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引用次数: 0
摘要
心脏病是导致全球死亡的主要原因,需要及早发现,以便及时采取有效的医疗干预措施。在本研究中,我们提出了一种基于机器学习的早期心脏病预测模型。该模型在加州大学欧文分校机器学习资料库(UCI)的数据集上进行训练,并采用 Extra Trees 分类器进行特征选择。为确保模型训练的稳健性,我们使用 StandardScaler 方法对数据集进行了标准化,从而保留了分布形状并减轻了异常值的影响。对于分类任务,我们引入了一种新方法,即串联混合集合投票分类法。这种方法结合了两个混合集合分类器,每个分类器都利用了支持向量机、决策树、K-近邻、逻辑回归、Adaboost 和 Naive Bayes 等基础分类器的不同子集。通过利用串联组合分类器,所提出的模型显示出了一些有前途的性能结果;特别是,它达到了 86.89% 的准确率。这些结果凸显了结合多个基础分类器的优势在早期心脏病预测问题上的功效,从而有助于及时进行医疗干预。
Heart Disease Prediction Using Concatenated Hybrid Ensemble Classifiers
Heart disease is a leading global cause of mortality, demanding early detection for effective and timely medical intervention. In this study, we propose a machine learning-based model for early heart disease prediction. This model is trained on a dataset from the UC Irvine Machine Learning Repository (UCI) and employs the Extra Trees Classifier for performing feature selection. To ensure robust model training, we standardize this dataset using the StandardScaler method for data standardization, thus preserving the distribution shape and mitigating the impact of outliers. For the classification task, we introduce a novel approach, which is the concatenated hybrid ensemble voting classification. This method combines two hybrid ensemble classifiers, each one utilizing a distinct subset of base classifiers from a set that includes Support Vector Machine, Decision Tree, K-Nearest Neighbor, Logistic Regression, Adaboost and Naive Bayes. By leveraging the concatenated ensemble classifiers, the proposed model shows some promising performance results; in particular, it achieves an accuracy of 86.89%. The obtained results highlight the efficacy of combining the strengths of multiple base classifiers in the problem of early heart disease prediction, thus aiding and enabling timely medical intervention.