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基于机器学习的皮肤恶性黑色素瘤患者生存率的预后预测

Yuxin Wang¹, Jiajia Liu¹, Huawei Wu¹

¹School of Software, Xinjiang University, Urumqi, China.

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概括

新的DeepSmote模型准确地预测皮肤恶性黑色素瘤 (CMM) 的存活率,优于现有的方法. 这种预后工具在各种患者数据集中展示了强大的概括性,以改善CMM患者的治疗结果.

科学领域:

在瘤学瘤学.
生物信息学是一种生物信息学.
医学统计医学统计

背景情况:

皮肤恶性黑色素瘤 (CMM) 是一个重要的健康问题,需要早期诊断和准确的生存预测.
有效的预后模型对于提高CMM患者的生存率至关重要.

研究的目的:

开发和验证一个卓越的预后模型,用于预测皮肤恶性黑色素瘤 (CMM) 的存活率.
为了比较新的DeepSmote模型与已建立的机器学习算法的性能.

主要方法:

从SEER数据库 (2004-2015) 中对5979名CMM患者进行了回顾性分析.
该SMOTE+DeepSurv (DeepSmote) 模型是使用TCGA数据集开发和验证的,列车测试分为7:3.
模型性能使用包括曲线下面面积 (AUC),准确度,精度,回忆和F1分数在内的指标进行评估,将DeepSmote与DeepSurv,XGBoost,LR,SVM,RF,KNN和DT进行比较.

主要成果:

在SEER和TCGA数据集上,DeepSmote模型表现出卓越的预测性能.
在SEER测试组中,DeepSmote实现了0.96的AUC,0.95的准确性,以及0.95的F1得分,用于1年生存预测.
在外部TCGA队列中,在3年和5年的预测中观察到一致的优越性 (AUC:0.91,准确度:0.88,F1得分:0.87),表明强度和通用性.

结论:

DeepSmote作为一个准确和可概括的预后工具,用于CMM生存预测.
该模型在多个数据集和评估指标中始终优于其他评估模型.
这一进步具有显著的潜力,可以改善临床决策和CMM护理中的患者管理.

关键词:

机器学习机器学习塞尔·塞尔 (Seer) 是一个看得见的人.在TCGA中,TCGA就是TCGA.皮肤性恶性黑色素瘤统计分析是一种统计分析.生存分析,生存分析.

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