一个可解释的人工智能框架,用于预测吸烟者的COPD风险
Xuchun Wang1, Yuchao Qiao1, Yu Cui1
1Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Taiyuan, 030001, P.R. China.
BMC public health
|November 6, 2023
概括
这项研究开发了一个AI框架,以识别高风险的慢性阻塞性肺病 (COPD) 吸烟者. 关键预测因素包括年龄,CAT得分,BMI和环境因素,使有针对性的预防策略成为可能.
科学领域:
- 肺部医学 肺部医学
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 早期慢性阻塞性肺病 (COPD) 的症状经常被忽视,延迟了关键的预防和治疗.
- 这限制了对有风险的个体及时干预的机会.
研究的目的:
- 开发一种可解释的人工智能 (AI) 框架,用于识别吸烟人口中高风险的COPD个体.
- 为推动COPD风险预测的因素提供可解释的见解.
主要方法:
- 数据预处理技术 (FAMD,Boruta,NRSBoundary-SMOTE) 解决了缺少的数据,高维度和类不平衡的问题.
- 评估了七个机器学习模型,CatBoost在平衡的数据集上表现出卓越的性能.
- 使用沙普利增量解释 (SHAP) 和部分依赖图 (PDP) 实现了模型解释性.
主要成果:
- 年龄和14个其他变量,包括CAT得分,BMI和环境因素,是吸烟者的重要COPD预测因素.
- CatBoost,随机森林和后勤回归模型在不平衡的数据上表现良好.
- CatBoost与NRSBoundary-SMOTE相结合,根据AUC,F1得分和G-平均值获得了最佳的分类性能.
结论:
- 该研究成功地整合了特征查,不平衡的数据处理和先进的ML,用于在吸烟者中早期识别COPD风险.
- 确定的COPD风险因素为吸烟人口的有针对性的查和自我管理策略提供了基础.
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