一种混合机器学习方法来提高急性尾炎的诊断准确度
Betül Keskinkılıç Yağız1, Yasemin Keskin2, Metin Yalaza3
1Department of General Surgery, Samsun Gazi State Hospital, Samsun-Türkiye.
概括
一个新的混合机器学习 (ML) 模型显著提高了急性尾炎的诊断准确性,超过了传统的阿尔瓦拉多分数. 这一进步有望减少不必要的手术,并通过更快,更可靠的诊断来提高患者护理.
科学领域:
- 医疗信息学 医疗信息学
- 手术诊断手术诊断的使用方法
- 医疗保健中的机器学习
背景情况:
- 急性尾炎是对右下象限疼痛的急诊室访问的常见原因.
- 非典型的表现可能导致误诊,导致不必要的手术和增加医疗保健成本.
- 提高急性尾炎的诊断准确度对于有效的患者管理至关重要.
研究的目的:
- 开发和评估一种混合机器学习 (ML) 模型,以提高急性尾炎的诊断准确度.
- 为了将混合ML模型的性能与已建立的阿尔瓦拉多分数进行比较.
- 评估ML在减少负尾切除率方面的潜力.
主要方法:
- 对395名因疑似急性尾炎而接受尾切除术的患者进行了回顾性分析.
- 收集人口统计,临床,实验室和放射学数据.
- 应用和评估各种ML算法,包括混合模型 (NaiveBayes,AdaBoost,RandomForest),使用十倍交叉验证.
- 使用精度,F测量,MCC,ROC面积和PRC面积进行性能评估.
主要成果:
- 混合ML模型实现了92.9%的准确性,93%的F测量和90.8%的ROC面积,超过了Alvarado评分 (79.0%的准确性).
- 该模型正确识别了95.6%的急性尾炎病例和75.9%的阴性尾切除病例.
- 组织病理学确认显示,86.3%的患者患有急性尾炎.
结论:
- 与阿尔瓦拉多分数相比,混合ML模型为急性尾炎提供了优越的诊断性能.
- 这种ML模型的临床整合可能会降低负尾切除的发生率.
- 实施先进的ML工具可以导致更有效,更准确的患者诊断和管理.
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