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使用先进的机器学习分类器预测冠心病,以改善心血管风险评估

Moiz Ur Rehman¹, Shahid Naseem¹, Ateeq Ur Rehman Butt²

¹Faculty of Information Sciences, Division of Science & Technology, University of Education, Lahore, Township Campus, 54770, Lahore, Pakistan.

Scientific reports

|April 17, 2025

概括

这项研究引入了一种先进的机器学习框架,用于预测冠心病 (CHD). 这种新型混合模型的准确率达到了97%,在早期CHD预测中表现优于传统方法.

科学领域:

心脏病学心脏病学
医疗信息学医疗信息学
人工智能的人工智能

背景情况:

冠心病 (CHD) 是全球主要的死亡原因.
早期预测心血管疾病对于有效的临床干预至关重要.
机器学习 (ML) 在提高心脏病诊断准确度方面表现有前途.

研究的目的:

开发一个全面的ML框架,以改善CHD预测.
解决医疗保健数据集中特征选择和类不平衡方面的挑战.
为CHD预测引入和评估一种新的混合PSO-ANN模型.

主要方法:

使用相互信息 (MI) 进行功能选择.
处理类不平衡与合成少数群体过量采样技术 (SMOTE).
开发一个混合粒子群集优化-人工神经网络 (PSO-ANN) 模型.

主要成果:

拟议的PSO-ANN模型实现了高达97%的预测准确性.
这超过了传统分类器的95.8%准确率,如物流回归和随机森林.
该框架有效地改善了特征选择,并解决了数据不平衡问题.

结论:

开发的ML框架为CHD预测提供了卓越的性能.
混合PSO-ANN模型显示了临床数据分析的巨大潜力.
这种方法提高了心血管健康的早期检测和决策.

关键词:

人工神经网络的人工神经网络冠状动脉心脏病冠状动脉心脏病医疗信息学医疗信息学健康问题健康问题机器学习机器学习医学图像医学图像互助信息互助信息互助信息粒子集群优化粒子集群优化

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