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一种混合方法与metaheuristic优化和随机森林改善心脏病预测

Geetha Narasimhan¹, Akila Victor²

¹School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, India.

Scientific reports

|March 31, 2025

概括

这项研究使用机器学习和元启发算法增强了心血管疾病预测. 基因算法优化随机森林 (GAORF) 实现了最高的准确性,改善了早期心脏病诊断和降低死亡率.

科学领域:

心脏病学心脏病学
人工智能的人工智能
计算生物学计算生物学

背景情况:

心血管疾病 (CVD) 是全球死亡的主要原因,目前在紧急情况下的诊断方法只能达到57%的疗效.
现有诊断实践的成本和局限性不断增加,需要改善早期预测心脏病的方法.

研究的目的:

开发一个智能机器学习 (ML) 模型,与元启发优化集成,用于准确的CVD预测.
提高心脏病诊断的准确性,从而旨在降低死亡率.

主要方法:

应用SelectKBest功能选择到克利夫兰心脏数据集.
评估了包括遗传算法优化随机森林 (GAORF),粒子群集优化随机森林 (PSORF) 和殖民地优化随机森林 (ACORF) 在内的元启发式优化技术.
优化前后的分类算法性能与SelectKBest过器方法相比较.

主要成果:

基因算法优化随机森林 (GAORF) 在克利夫兰心脏数据集上表现出卓越的性能.
与其他评估的优化和分类技术相比,拟的GAORF模型实现了最高的准确性.
使用SelectKBest的特征选择为数据集分析提供了整体排名.

结论:

优化的机器学习模型,特别是GAORF,显著提高了心血管疾病预测的准确性.
整合元启发算法提供了一个有希望的方法,以提高心脏病的诊断能力.
通过先进的计算方法,对心血管疾病的早期和准确预测可以有助于降低患者死亡率.

关键词:

殖民地优化 (ACO) 是一种方法.功能选择功能选择遗传算法 (GAO) 是一种基因算法.这是一种元启发式 (metaheuristic) 启发式.优化技术的优化技术粒子群集优化 (PSO) 是一种随机森林 (RF) 是一个随机的森林.

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