可以解释的AI驱动的智能系统用于心血管疾病的精确预测
Anas Bilal1, Abdulkareem Alzahrani2, Khalid Almohammadi3
1College of Information Science and Technology, Hainan Normal University, Haikou, China.
Frontiers in medicine
|July 24, 2025
概括
本研究介绍了一种可解释的人工智能 (XAI) 系统,用于预测心血管疾病 (CVD),增强医疗保健的信任和准确性. XAI方法提高了预测可靠性,帮助临床医生在患者护理决策中.
科学领域:
- 人工智能在医学中的应用
- 心血管疾病研究研究
- 医疗保健信息学 医疗保健信息学
背景情况:
- 心血管疾病 (CVD) 构成了重大的全球健康挑战,早期准确预测的困难使其复杂化.
- 传统的CVD预测机器学习模型经常充当"黑子",限制了临床信任和可用性.
- 可解释的人工智能 (XAI) 提供了一个潜在的解决方案,通过为AI决策过程提供透明度.
研究的目的:
- 通过使用可解释的人工智能 (XAI) 来引入心血管事件的智能预测系统.
- 解决传统,不透明的机器学习模型在预测心血管疾病方面的局限性.
- 提高AI驱动的预测在临床环境中的可靠性和可用性.
主要方法:
- 开发了一个智能预测系统,将先进的机器学习算法与XAI集成在一起.
- 利用Kaggle的308,737名患者记录的综合数据集,包括人口统计,临床测量和生活方式因素.
- 应用XAI技术,为人工智能驱动的心血管事件预测提供可理解的解释.
主要成果:
- 拟议的XAI系统在预测心血管事件方面实现了91.94%的准确性.
- 与以前的方法相比,该系统的失误率降低了8.06%.
- XAI集成提高了医疗保健专业人员对AI预测的透明度和可信度.
结论:
- 可解释的人工智能 (XAI) 显著提高了心血管疾病预测的透明度和可靠性.
- 开发的XAI系统改善了临床决策,从而改善了患者的护理和治疗.
- 通过增加人工智能工具的信任和可用性,XAI具有很大的潜力来推进心血管医疗保健.
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