心脏病预测与一个功能敏感的可解释框架,用于医疗物联网传感器传感器
Nallakaruppan Kailasanathan1, Gangadevi Ezhilarasan2, Shitharth Selvarajan3,4,5
1Balaji Institute of Modern Management, Sri Balaji University, Pune, India.
Frontiers in digital health
|October 17, 2025
概括
这项研究开发了一个可解释的机器学习框架,使用身体的生命体征来预测早期心脏病. 随机森林模型实现了0.955准确度,为更好的心血管医疗保健决策提供了透明的见解.
科学领域:
- 心血管健康 心血管健康
- 医疗保健中的机器学习
- 可解释的人工智能
背景情况:
- 现代生活方式 (肥胖,吸烟,压力,高血压,久坐) 会增加心血管疾病的风险.
- 流行病后的健康趋势和药物副作用导致早期心力衰竭病例的增加,特别是在25-40岁的成年人中.
- 对于用于早期心脏病预测的自动化,可解释的系统有着至关重要的需求.
研究的目的:
- 开发和评估用于早期心脏病预测的自动化,可解释的机器学习框架.
- 评估各种机器学习模型的性能,使用二级身体的重要数据.
- 通过可解释的人工智能技术来提高模型透明度.
主要方法:
- 使用了身体生命体征的二级数据集.
- 使用的机器学习分类器:支持矢量机,随机森林,决策树和物流回归.
- 使用精度,F1得分和k倍交叉验证评估模型性能;使用本地可解释模型-不可知解释 (LIME) 和SHAP值进行解释性.
主要成果:
- 随机森林分类器实现了最高的性能,精度和F1得分为0.955.
- 当地可解释模型不可知解释 (LIME) 和SHAP值为模型预测和特征贡献提供了明确的见解.
- SHAP决策图表可视化了分类行为和关键特征影响.
结论:
- 拟议的可解释机器学习框架可以准确预测心脏病,同时确保透明度.
- 传感器数据,云分析和可解释的人工智能的整合增强了医疗保健中的决策.
- 这种方法有助于减少早期心力衰竭的发生率,并改善患者的治疗结果.
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