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一个新的预后模型的食道状细胞癌基于云-最小平方支向量机器

Ke Liu^1,2, Liu-Qing Shen¹, Dian-Bao Zhang¹

¹Henan Key Laboratory of Microbiome and Esophageal Cancer Prevention and Treatment, Henan Key Laboratory of Cancer Epigenetics, Cancer Hospital, The First Affiliated Hospital (College of Clinical Medicine) of Henan University of Science and Technology, Luoyang, China.

Journal of thoracic disease

|October 23, 2023

概括

这项研究开发了一种新的Cloud-LSSVM模型用于食道状细胞癌 (ESCC) 预后,显著提高了对现有方法的预测准确性. 优化的模型增强了确定不确定的预后因素以获得更好的患者结果的能力.

科学领域:

在瘤学瘤学.
机器学习机器学习
生物统计学生物统计学

背景情况:

目前食道状细胞癌 (ESCC) 预后模型缺乏足够的准确性.
现有的模型很难有效地确定不确定的预后因素.
对于ESCC,需要一个高精度的预后模型.

研究的目的:

使用云模型优化最小平方支持向量机 (LSSVM) 算法.
确定并将不确定的预后因素纳入新的ESCC预后模型.
为ESCC建立一种新的,高精度的预后模型.

主要方法:

利用来自SEER数据库 (培训) 的4771名ESCC患者和来自HCDC数据库 (验证) 的635名ESCC患者的大数据集.
进行了生存分析,包括日志等级,单变量和多变量考克斯分析,以确定独立的风险因素.
开发并验证了预测模型:名图,随机森林和拟议的云-LSSVM.

主要成果:

多变量考克斯分析确定了年龄,性别,种族,分化等级和病理T和M类别作为ESCC的显著预后因素.
与差异化等级 (0.548,0.506),随机森林 (0.649,0.498) 和名ogram (0.659,0.563) 相比,云-LSSVM模型显示出更高的准确性 (C指数为0.71,0.689).
新模型有效地将云模型的随机性和模糊性与LSSVM的学习能力相结合.

结论:

开发的云-LSSVM模型显示出与随机森林和名ogram模型相比的显著预测优势.
这种新的方法解决了由样本差异产生的预测准确性挑战.
云-LSSVM模型为ESCC预后提供了一个更准确,更可靠的工具.

关键词:

云最小方形支持向量机 (云-LSSVM)食管状细胞癌 (ESCC) 是一种监测,流行病学和最终结果 (SEER)机器学习是机器学习.预后预测预测的预测.

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