基于机器学习的预测在结肠直肠多中对组织病理学分类
Gökhan Koker1, Gizem Zorlu Gorgulugil1, Muhammed Ali Coskuner2
1Department of Internal Medicine, University of Health Sciences, Antalya Training and Research Hospital, Antalya, Türkiye.
机器学习模型可以使用患者数据预测结直肠多类型. 这使得个性化查策略能够超越标准的基于年龄的协议,以更好地预防癌症.
科学领域:
- 胃肠病学 胃肠病学
- 在瘤学瘤学.
- 数据科学数据科学数据科学
背景情况:
- 结肠直肠多是结肠直肠癌的前体,需要准确的组织病理学分类来进行风险评估.
- 预测多类型有助于早期临床管理和个性化查策略.
- 机器学习 (ML) 模型提供了利用可访问的患者数据预测多重体组织病理学的潜力.
研究的目的:
- 评估ML算法在预测结肠直肠多病原学类型方面的有效性.
- 确定聚菌组织病理学的关键人口,临床和饮食预测因素.
- 探索ML在个性化结直肠癌查中的潜力.
主要方法:
- 对491名首次接受结肠镜检查的患者进行了回顾性分析.
- 应用四个ML算法:决策树,随机森林,支持矢量机 (SVM) 和极端梯度增强.
- 使用准确度,灵敏度,特异性和SHapley添加剂对变量重要性的解释来评估模型性能.
主要成果:
- 机器学习模型的预测准确度从70.9%到76.4%不等,SVM和随机森林表现最高.
- "无多"组的预测准确度很高 (85.6%-95.9%的灵敏度).
- 经常食用布尔格,摄入红肉,年龄和BMI被确定为重要的预测因素.
结论:
- 从常规数据中,ML算法可以准确地预测结肠直肠多的组织病理类型.
- 这种方法支持个性化查,超越传统的基于年龄的指导方针.
- 将ML整合到查方案中可以提高结直肠癌前体的早期检测和管理.
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