基于机器学习算法对结直肠癌死亡率的分类和诊断预测:一个多中心国家研究
Gohar Mohammadi1, Mehdi Azizmohammad Looha2, Mohammad Amin Pourhoseingholi3
1Cancer Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Asian Pacific journal of cancer prevention : APJCP
|January 29, 2024
概括
机器学习模型使用诊断时间和瘤特征等关键因素准确预测结直肠癌 (CRC) 存活率. 纯粹的贝叶斯模型显示了CRC患者死亡率预测的最佳疗效.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
背景情况:
- 结肠直肠癌 (CRC) 是全球癌症死亡的主要原因.
- 准确预测CRC患者的存活率对于有效的治疗计划至关重要.
- 机器学习 (ML) 为提高瘤学预后准确性提供了有希望的工具.
研究的目的:
- 开发和评估机器学习模型,用于预测结直肠癌 (CRC) 患者的生存结果.
- 确定CRC存活率的关键临床和人口预测因素.
- 在CRC生存预测中比较各种ML算法的性能.
主要方法:
- 对来自伊朗三级医院的1853名CRC患者的回顾性分析.
- 开发和评估六个ML模型:逻辑回归,天真贝叶斯,SVM,NN,DT和LGBM.
- 使用随机森林进行特征选择和通过十倍交叉验证和AUC进行性能评估.
主要成果:
- 从诊断开始的时间,年龄,瘤大小,转移状态,淋巴结参与和治疗类型被确定为显著的生存预测因素.
- 原始贝叶斯 (NB) 和光梯度增强机 (LGBM) 模型实现了最高的预测准确性,AUC为0.70.
- 该NB模型证明了最佳的死亡率预测,具有平衡的灵敏度和特异性.
结论:
- 诊断时间,年龄和瘤特征等临床变量对于预测CRC生存至关重要.
- 纯粹的贝叶斯模型在预测CRC患者的死亡率方面表现出很高的有效性.
- 建议包括早期诊断,整合数字健康记录,并将预后变量纳入治疗指南.
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