使用集体学习和基于生活方式和人口统计数据的XAI进行可解释的肺癌风险预测
Shahid Mohammad Ganie1, Pijush Kanti Dutta Pramanik2
1AI Research Centre, Department of Analytics, Woxsen University, Hyderabad, Telangana 502345, India.
Computational biology and chemistry
|April 2, 2025
概括
这项研究使用集体学习和可解释AI (XAI) 增强了肺癌预测. 堆叠组合模型实现了99.59%的准确性,改善了早期检测和患者的结果.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能的人工智能
- 在瘤学瘤学.
背景情况:
- 肺癌仍然是全球癌症死亡的主要原因.
- 早期和准确的检测对于改善患者存活率至关重要.
- 传统的预测模型往往缺乏临床使用所需的准确性和可解释性.
研究的目的:
- 通过集合学习方法提高肺癌预测的准确性.
- 整合可解释的人工智能 (XAI) 技术,以实现模型解释性和临床信任.
- 确定影响肺癌发展的关键临床和人口风险因素.
主要方法:
- 在三个真实世界肺癌数据集上实施先进的组合学习技术 (投票,堆叠).
- 使用全面指标 (准确性,精度,回忆,F1分数,AUC,卡帕,MCC) 评估模型性能.
- 整合SHAP (夏普利增量解释) 值,用于特征重要性分析和模型可解释性.
主要成果:
- 与传统方法相比,整体模型显著提高了肺癌预测准确度.
- 堆叠组合模型实现了平均准确率为99.59%,精度为100%和AUC为100%.
- 统计验证 (弗里德曼测试,霍尔姆后期) 证实了堆叠组合模型的优越性能.
- XAI确定了关键的临床和人口因素,为肺癌风险提供了洞察力.
结论:
- 集体学习模型,特别是堆叠,为肺癌预测提供了卓越的准确性和可靠性.
- 可解释人工智能 (XAI) 提高了模型的解释性,促进了临床信任和潜在的采用.
- 这些发现支持开发有针对性的干预措施和改善肺癌风险管理策略.
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