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使用SEER数据对机器学习和统计生存模型进行比较研究,以提高宫癌预后和风险因素评估,使用SEER数据

Anjana Eledath Kolasseri1, Venkataramana B2

  • 1Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, Tamil Nadu, India.

Scientific reports
|September 27, 2024
PubMed
概括

机器学习,特别是随机生存森林 (RSF),提供了更高的生存预测准确度,并与传统统计模型相比,识别了宫癌的关键风险因素. 为了临床应用,需要进一步的研究.

关键词:
宫癌是发生在宫癌的原因之一.机器学习 机器学习预测因素 预测因素统计方法 统计方法对生存分析的分析.

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