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用机器学习技术和先进的参数优化方法预测结肠直肠癌患者的生存率的新方法

Andrzej Woźniacki¹, Wojciech Książek¹, Patrycja Mrowczyk²

¹Department of Computer Science, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Warszawska 24, 31-155 Cracow, Poland.

|September 28, 2024

概括

人工智能模型对预测结直肠癌存活率和死亡率显示出希望. 机器学习分类器的准确率约为80%,有助于早期诊断和治疗支持.

科学领域:

在瘤学瘤学.
生物统计学生物统计学
机器学习机器学习

背景情况:

结肠直肠癌 (CRC) 是全球癌症死亡的主要原因.
在50岁以下的成年人中,CRC诊断的增加令人担忧.
像人工智能 (AI) 这样的先进技术对于改善CRC结果至关重要.

研究的目的:

开发和评估基于人工智能的分类模型,用于预测结直肠癌患者的生存率.
评估各种机器学习算法在预测死亡率方面的性能.
确定用于瘤学临床决策支持的有效AI工具.

主要方法:

使用了八种机器学习分类器:随机森林,XGBoost,CatBoost,LightGBM,梯度提升,额外树,k-最近邻居 (KNN) 和决策树.
使用Optuna,RayTune和HyperOpt框架进行了算法优化.
这项研究利用了来自巴西的大型公共数据集,包括数万份患者记录.

主要成果:

开发的模型在预测一年,三年和五年生存率方面取得了很高的准确性.
模型准确预测了整体死亡率和癌症特定死亡率.
性能最好的分类器 (CatBoost,LightGBM,梯度提升,随机森林) 达到约80%的准确性.

结论:

为结直肠癌预后开发了有效的AI分类模型.
这些模型显示了将其整合到临床实践中的潜力.
这项研究支持人工智能用于增强结直肠癌患者管理.

关键词:

通过HyperOpt,我们可以获得超视觉.这就是奥普图纳.在 RayTune 里面,我们可以找到 RayTune.结肠直肠癌是什么意思参数优化的参数优化生存预测的预测.

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