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使用临床和人口学描述符,机器学习对部唾液腺瘤的分类

Luís Arthur de Melo Tassinari¹, Anna Luíza Damaceno Araújo^2,3, Sebastião Silvério de Sousa-Neto⁴

¹Institute of Science and Technology (ICT-UNIFESP), Federal University of São Paulo, São José Dos Campos, São Paulo, Brazil.

|November 18, 2025

概括

机器学习模型对 palatal 唾液腺瘤的分类有前途,由于罕见的恶性亚型,实现高特异性但敏感性有限. XGBoost 展示了最好的性能.

科学领域:

在瘤学瘤学.
医疗信息学医疗信息学
计算生物学计算生物学

背景情况:

宫唾液腺瘤代表了一组多样化的瘤.
准确的临床分类对于适当的患者管理和治疗计划至关重要.

研究的目的:

评估机器学习 (ML) 模型在分类 palatal 唾液腺瘤中的有效性.
评估人口统计和临床数据对使用ML进行瘤分类的有用性.

主要方法:

四个ML模型 (XGBoost,MLP,SVM,RF) 在100名患者的数据上进行了训练和测试.
该研究采用超参数优化 (网格搜索) 和五倍交叉验证.
使用准确度,宏观平均灵敏度,特异性,精度和F1分数来衡量性能.

主要成果:

XGBoost和MLP实现了最高的平均精度 (81%),其次是SVM (80%) 和RF (79%).
所有模型都表现出高特异性 (85% - 90%),但宏观平均敏感性低,F1分数低 (<75%).
模型的性能在多形腺瘤 (PA) 中很好,但在较罕见的恶性瘤中下降.

结论:

ML是 palatal 唾液腺瘤分类的可行工具,提供高特异性.
敏感性有限的原因是罕见恶性瘤子类的代表性不足.
XGBoost 成为了最强大的和计算效率最高的模型.

关键词:

人工智能的人工智能是人工智能.超参数优化超参数优化机器学习是机器学习.唾液腺瘤唾液腺瘤转移学习转移学习

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