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使用胆结石数据确定恶性瘤风险因素,并比较机器学习方法来预测恶性瘤

Sirin Cetin¹, Ayse Ulgen^2,3, Ozge Pasin⁴

¹Department of Biostatistics, Faculty of Medicine, Amasya University, Amasya 05100, Türkiye.

Journal of clinical medicine

|September 13, 2025

概括

胆结石疾病与恶性瘤风险有关. 先进的机器学习模型,特别是 Tabular Prior-data Fitted Network (TabPFN),有效地识别了恶性瘤的关键预测因素,如年龄,BMI和HRT史.

科学领域:

消化系统疾病消化系统疾病
在瘤学瘤学.
计算生物学是一种计算生物学.

背景情况:

胆结石病是一种常见的疾病,具有多因素的风险因素.
胆结石的一些危险因素也可能增加癌症风险.
了解这些联系对于早期发现和预防至关重要.

研究的目的:

为了调查胆结石病与恶性瘤之间的关联.
为此分析应用先进的机器学习 (ML) 算法.
为了确定胆结石患者恶性瘤的显著预测因素.

主要方法:

对大约1000名患者数据集的分析.
使用了六个ML算法:随机森林,SVM,MLP,MLP_PT,天真贝叶斯和TabPFN.
使用AUC,精度,灵敏度和特异性等指标评估性能.

主要成果:

年龄,体重指数 (BMI) 和荷尔蒙替代疗法 (HRT) 史被确定为恶性瘤的关键预测因素.
表格式预先数据装配网络 (TabPFN) 模型表现出卓越的性能.
在预测恶性瘤方面,TabPFN实现了高准确性和有效性.

结论:

尖端的ML方法可以揭示复杂的临床数据关系.
TabPFN在与胆结石相关的恶性瘤的预测建模方面显示出显著的前景.
研究结果为个性化患者管理和风险评估提供了洞察力.

关键词:

临床见解临床见解胆结石疾病胆结石疾病机器学习是机器学习.恶性瘤是一种恶性瘤.预测建模预测建模

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