基于机器学习的分类:一种有希望的诊断方法和治疗优化方法
Mehmet Vehbi Kayra1, Ali Şahin2, Serdar Toksöz3
1Department of Urology, Adana Dr. Turgut Noyan Application and Research Center, Baskent University, Adana, Turkey.
Andrology
|October 3, 2024
概括
机器学习模型使用超声波准确地评分瘤,优于传统方法. 这种方法为自动化临床预测提供了潜力,改善了男性不孕症治疗和患者的生活质量.
科学领域:
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
- 医疗成像医学成像
- 机器学习 机器学习
背景情况:
- 静脉是一种可治疗的男性不孕症原因.
- 与超声波相比,体检对变水的分级产生不一致的结果.
- 精确的肌肉变形细胞分级对于有效的不孕症管理至关重要.
研究的目的:
- 开发一个多类的机器学习模型,用于使用超声波测量对变形肌瘤进行分级.
- 评估不同机器学习模型在Varicocoele分级中的性能.
- 为了确定最有影响力的超声波参数和患者位置,以准确分级.
主要方法:
- 使用Dubin和Amelar系统评估了248名单边静脉瘤患者.
- 血管直径和反流时间的超声波测量是在卧卧和站立的位置进行的.
- 开发和评估了四种多类机器学习模型 (随机森林,SVM,MLP,KNN).
主要成果:
- 随机森林 (RF) 模型实现了最高的准确性 (0.81 ± 0.06) 和F1得分 (0.79 ± 0.02).
- 在仰卧姿势的丸顶部的血管直径是最有影响力的测量.
- 不同的模型显示,根据患者的位置和投射,对反流时间测量的依赖程度各不相同.
结论:
- 与传统方法相比,机器学习模型在变菌分级方面提供了更高的准确性.
- 使用ML来自动预测静脉等级显示出临床应用的前景.
- 个性化的静脉瘤分级可能会改善治疗结果和患者的生活质量.
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