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基于决策树的学习和实验室数据挖掘:一种高效的阿米比亚सिस测试方法.

Enas Al-Khlifeh1, Ahmad S Tarawneh2, Khalid Almohammadi3

  • 1Department of Applied Biology, Al-Balqa Applied University, Salt, Jordan. Al-khlifeh.en@bau.edu.jo.

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机器学习使用电子医疗记录准确预测阿米比亚सिस,比传统显微镜更好地进行诊断. 这有助于区分寄生虫感染和胃肠炎.

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E. histolytica 进行分析.亚美比亚症是什么?决策树 决策树是一个决策树.电子医疗记录 (EMR) 是一种电子医疗记录.选择功能选择功能选择.约旦 约旦 约旦 约旦血清细胞溶解症 血清细胞溶解症机器学习是机器学习.显微镜诊断的诊断方法便中的红血细胞.

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科学领域:

  • 医疗信息学医学信息学
  • 计算生物学是一种计算生物学.
  • 寄生虫学的寄生虫学

背景情况:

  • 阿米比亚सिस是一种全球性的健康问题,通常通过显微镜诊断,可以与胃肠炎混.
  • 准确的诊断至关重要,特别是在感染率高的发展中国家.

研究的目的:

  • 开发一种机器学习 (ML) 模型,用于准确预测阿米比亚सिस.
  • 利用实验室发现和人口统计数据进行自动诊断.

主要方法:

  • 在约旦电子医疗记录 (2020-2022) 上训练了八个决策树算法.
  • 包括763个阿米比克和314个非阿米比克病例,分析人口统计,临床症状和实验室结果.
  • 使用特征排名和相关性来提高分类准确性.

主要成果:

  • 阿米比亚सिस的关键预测因素包括中性粒细胞百分比,粘液存在以及便中的红细胞/白细胞计数.
  • 决策树模型达到了92%-94.6%的准确度;优化的随机森林模型显示了98%的AUC.
  • 在约旦,阿米比亚斯占胃肠炎病例的17.22%,男性和幼儿的发病率更高.

结论:

  • 应用于电子医疗记录的机器学习准确地预测了amebiasis.
  • 这支持了ML作为诊断寄生虫疾病的决策支持工具的作用.