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使用机器学习预测慢性增多性候群病逆角性粘膜

Omid Moztarzadeh1,2, Jan Liska1, Veronika Liskova1

  • 1Department of Stomatology, University Hospital Pilsen, Faculty of Medicine in Pilsen, Charles University, Alej Svobody 80, 30460 Pilsen, Czech Republic.

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概括
此摘要是机器生成的。

机器学习准确地预测慢性超塑性候群病 (CHC) 的发生,特别是在后角粘膜. 这种方法提高了这种罕见的口腔真菌感染的诊断能力.

关键词:
候选症/慢性多发性候选症慢性粘膜病变 慢性粘膜病变数字健康数字健康血清白血病 (leukoplakia) 是一种病.机器学习是机器学习.口腔内皮层瘤是口腔内皮层瘤口腔状细胞癌的癌症.

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科学领域:

  • 口腔病理学 口腔病理学
  • 菌类学 菌类学是指菌类学.
  • 计算生物学是一种计算生物学.

背景情况:

  • 慢性多发性角膜炎 (CHC) 是一种罕见的口腔感染,呈现为白色/红色粘膜斑块.
  • 由于外观相似,病变可能被误诊为白血病或红质白血病.
  • 主要的CHC位置包括舌头,后角和口腔粘膜.

研究的目的:

  • 调查解剖位置,特别是背角粘膜对CHC发展的影响.
  • 开发一种机器学习 (ML) 模型,根据风险因素预测CHC的发生.
  • 提高诊断的准确性,并为心血管疾病的治疗策略提供信息.

主要方法:

  • 使用渐变增强回归 (GBR),一种机器学习算法.
  • 通过分析关键风险因素来分类CHC病变位置.
  • 使用MSE,RMSE,R-squared和MAE评估模型性能.

主要成果:

  • 该ML模型成功地预测了CHC在背角粘膜中的发生.
  • GBR方法在分类损伤位置方面表现出高准确度.
  • 性能指标证实了分类方法的稳定性和可靠性.

结论:

  • 机器学习为预测CHC位置提供了一种有价值的工具,特别是在回角区域.
  • 拟议的ML技术有助于提高CHC的诊断准确性.
  • 这项研究为临床诊断和进一步研究CHC提供了洞察力.