通过可解释的AI增强基于机器学习的慢性病预测
Sanjana Singamsetty1, Swetha Ghanta1, Sujit Biswas2,3
1Department of Computer Science and Engineering, School of Engineering and Sciences, SRM University, AP, Guntur, Andhra Pradesh, India.
PeerJ. Computer science
|December 9, 2024
概括
这项研究开发了一个准确的机器学习模型,用于早期慢性病 (CRD) 预测. 后勤回归实现了99.07%的准确性,通过及时干预实现了更好的患者结果.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 预测分析是一种预测分析.
背景情况:
- 慢性脏病 (CRD) 构成了重大的医疗保健挑战,需要早期和准确的预测以获得有效的治疗.
- 当前的诊断方法可能缺乏及时干预所需的速度和准确性.
研究的目的:
- 开发和评估一个端到端的预测模型,用于CRD的二进制分类.
- 通过超参数优化增强模型性能,并比较各种机器学习算法.
- 整合可解释的人工智能 (XAI) 以提高模型可解释性.
主要方法:
- 实现一个端到端的机器学习管道用于CRD预测.
- 使用 GridSearchCV 进行超参数优化.
- 评估随机森林,额外树木分类器,物流回归 (L2罚款) 和人工神经网络 (ANN).
- 应用了像LIME和SHAP这样的XAI技术来实现模型的解释性.
主要成果:
- 在多个模型中实现了99.07%的高预测准确度.
- 后勤回归与L2处罚显示出优越和一致的性能.
- XAI技术为预测模型的决策过程提供了洞察力.
结论:
- 开发的端到端预测模型显示了早期CRD检测的巨大潜力.
- 优化的机器学习模型,特别是逻辑回归,在CRD分类中提供了高精度.
- 整合XAI提高了对临床环境中的预测模型的信任和理解,支持知情决策.
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