使用机器学习来预测尿盆结阻塞儿童手术的必要性
Çiğdem Arslan Alici1, Baran Tokar2
1Health Sciences University Eskisehir City Hospital, Pediatric Urology Clinic, Eskisehir, Turkey. lakapies26@gmail.com.
Irish journal of medical science
|January 27, 2025
概括
机器学习模型准确地预测了在尿路皮层结口阻塞诱导的水缩中需要进行手术的情况. XGBC分类器实现了95.4%的准确性,有助于为更好的患者护理做出临床决策.
科学领域:
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 泌尿盆腔结口的阻塞会导致水,导致潜在的损伤.
- 在这些情况下预测治疗的必要性会带来临床挑战.
研究的目的:
- 评估机器学习模型,特别是XGBClassifier和Logistic Regression,用于预测水症患者的治疗必要性.
主要方法:
- 对水缩患者病历 (2015-2020年) 的回顾性分析.
- 分类为经营 (129) 和非经营 (194) 组.
- 利用XGBC分类器和后勤回归来预测手术干预,通过ROC-AUC,灵敏度和特异性评估性能.
主要成果:
- XGBClassifier表现出优异的预测,ROC-AUC的准确度为0.977和95.4%.
- 在交叉验证过程中,物流回归显示出高预测性.
- 确定了关键的预测因素:光阴学阻塞,脏大小,脏骨盆前后直径和超声波上的体厚度.
结论:
- 机器学习算法在预测由于阻塞而导致水缩的手术干预方面提供了高准确度和灵敏度.
- 这些模型可以支持临床决策,改善患者管理和外科手术需求预测.
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