利用混合机器学习提高心脏病预测的准确性。

IF 1.7 4区医学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-06-02 DOI:10.1080/10255842.2025.2510368

Huie Zhang, Caihong Li, Xinzhi Tian, Haijie Shen

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引用次数: 0

摘要

在这项研究中，使用自适应增强（ADA）和直方图梯度增强（HGB）机器学习模型进行心血管疾病预测。为了提高模型的预测精度，将元启发式优化算法海马优化器（SHO）和混沌博弈优化器（CGO）集成到模型中。这导致了混合模型的发展：ADSH （ADA + SHO）、ADCG （ADA + CGO）、HGSH （HGB + SHO）和HGCG （HGB + CGO）。其中，HGSH模型的准确率最高，达到0.912，优于其他模型。HGCG次之，精度为0.902，基础ADA模型性能较差，精度为0.840。

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Enhancing heart disease prediction accuracy with hybrid machine learning.

In this study, cardiovascular disease prediction was performed using adaptive boosting (ADA) and histogram gradient boosting (HGB) machine learning models. To improve their predictive accuracy, metaheuristic optimization algorithms, the Sea-Horse Optimizer (SHO) and the Chaos Game Optimizer (CGO), were integrated with the models. This led to the development of hybrid models: ADSH (ADA + SHO), ADCG (ADA + CGO), HGSH (HGB + SHO), and HGCG (HGB + CGO). Among them, the HGSH model achieved the highest accuracy of 0.912, outperforming the others. HGCG followed with 0.902, while the base ADA model showed lower performance with a precision of 0.840.

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来源期刊

Computer Methods in Biomechanics and Biomedical Engineering 工程技术-工程：生物医学

CiteScore

4.10

自引率

6.20%

发文量

179

审稿时长

4-8 weeks

期刊介绍： The primary aims of Computer Methods in Biomechanics and Biomedical Engineering are to provide a means of communicating the advances being made in the areas of biomechanics and biomedical engineering and to stimulate interest in the continually emerging computer based technologies which are being applied in these multidisciplinary subjects. Computer Methods in Biomechanics and Biomedical Engineering will also provide a focus for the importance of integrating the disciplines of engineering with medical technology and clinical expertise. Such integration will have a major impact on health care in the future.