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机器学习预测前列腺腺癌的整体存活率,使用组合技术

Declan Ikechukwu Emegano¹, Mubarak Taiwo Mustapha², Dilber Uzun Ozsahin³

¹Operational Research Center in Healthcare, Near East University, Mersin 10, 99138, Nicosia, TRNC, Turkey; Department of Biomedical Engineering, Near East University, Mersin 10, Nicosia, TRNC, 99138, Turkey.

Computers in biology and medicine

|March 13, 2025

概括

渐变增强模型准确地预测前列腺腺癌存活率. 这种机器学习方法为患者的结果提供了更高的准确性,在生存预测中表现优于其他方法. 建议进行进一步的临床研究.

科学领域:

在瘤学瘤学.
生物信息学是一种生物信息学.
机器学习机器学习

背景情况:

前列腺腺癌 (PAC) 是男性的全球领先癌症,呈现出多种不同的亚型和预后挑战.
预测PAC的整体存活率 (OS) 是很困难的,因为疾病异质性,并发病症和当前标志物的局限性.

研究的目的:

评估整体机器学习 (ML) 模型,用于预测前列腺腺癌 (PAC) 患者的整体存活率 (OS).
确定最有效的ML模型,以提高PAC中的OS预测准确度.

主要方法:

使用了癌症基因组图谱 (TCGA) 全癌症图谱数据集.
评估了八个整体ML模型:随机森林,AdaBoost,梯度提升 (GB),XGBoost,LightGBM,CatBoost,硬投票分类器和支持矢量分类器.
使用准确度,精度,回忆,F-1分数和ROC AUC分数来评估模型性能.

主要成果:

梯度提升 (GB) 在准确性,精度,回忆和F-1分数方面获得了完美的分数 (1.0),ROC AUC分数为0.99,优于所有其他模型.
随机森林 (RF) 和AdaBoost也表现出强的表现,表明它们的潜在临床实用性.
整合ML技术显著提高了PAC存活率的预测精度.

结论:

整体机器学习模型,特别是渐变增强,在预测前列腺腺癌整体存活率方面表现出很高的有效性.
该研究强调了整体ML在改善PAC等复杂癌症的预后准确性方面的价值.
需要在临床环境中进行进一步的研究和验证,以将这些预测模型纳入患者护理.

关键词:

癌症癌症癌症癌症机器学习模型的机器学习模型前列腺前列腺前列腺前列腺腺癌前列腺腺癌.幸存的生存方式

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