心血管疾病检测:一种混合机器学习-AI框架,用于个性化诊断和风险评估
Medhat A Tawfeek1, Ibrahim Alrashdi1, Madallah Alruwaili2
1Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka Aljouf, 72388, Saudi Arabia.
PloS one
|October 29, 2025
概括
这项研究引入了一种混合机器学习和人工智能框架,用于早期心血管疾病诊断. 先进的模型实现了高精度,使个性化治疗和改善患者的结果.
科学领域:
- 计算生物学是一种计算生物学.
- 医疗信息学医学信息学
- 医疗保健中的人工智能
背景情况:
- 心血管疾病 (CVD) 仍然是全球主要的死亡原因,需要改进诊断和治疗工具.
- 传统的查方法往往缺乏个性化,导致患者的结果低于最佳.
- 心血管疾病诊断的复杂性需要先进的计算方法来准确评估风险和早期干预.
研究的目的:
- 开发一个混合计算框架,集成支持向量机 (SVM),粒子集群优化 (PSO) 和夏普利添加式扩展 (SHAP) 以提高心血管疾病诊断.
- 利用各种患者数据,包括电子健康记录,医学图像和基因组数据,进行全面的患者建模.
- 通过准确的疾病预后,识别高风险个体和促进个性化治疗策略来改善临床决策.
主要方法:
- 开发一个数学模型来解决心血管疾病的诊断复杂性.
- 整合支持矢量机 (SVM) 分类器用于疾病预测.
- 粒子群集优化 (PSO) 的应用,用于SVM模型的超参数调整.
- 利用夏普利添加式解释 (SHAP) 来基于人工智能的解释和理解诊断预测.
- 该框架的培训和验证使用来自电子健康记录,医疗图像和基因组数据的各种患者数据.
主要成果:
- 拟议的混合ML-AI框架在MIMIC-III临床数据库 (v1.4) 上取得了卓越的性能.
- 关键性能指标包括高精度 (98.4%),精度 (97.5%),回忆 (96.4%),F1评分 (96.9%) 和AUC-ROC (97.35%).
- 该框架表现出强大的诊断能力,具有高灵敏度 (96.4%),特异性 (98.7%) 和低负面概率比率 (0.036),有效识别高风险和低风险患者.
结论:
- 混合ML-AI框架为心血管疾病的早期检测和风险分层提供了重大进展.
- 这种方法促进了个性化治疗策略,可能降低与无效疗法相关的医疗保健成本.
- 该框架通过提高预测能力来增强医疗保健服务,最终旨在改善患者的结果和生活质量.
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