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相关概念视频

Hepatitis01:25

Hepatitis

Hepatitis is an inflammatory condition of the liver most commonly caused by hepatotropic viruses (A–E), though non-infectious causes such as alcohol and drugs also exist.Hepatitis AHepatitis A virus (HAV) is a non-enveloped RNA virus of the Picornaviridae family. It is primarily transmitted via the fecal-oral route, typically through ingestion of contaminated food or water. After ingestion, HAV enters the bloodstream through the oropharynx or intestinal epithelium and reaches the liver. The...

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Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
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在埃及优化HCV疾病预测:hyOPTGB框架

Ahmed M Elshewey1, Mahmoud Y Shams2, Sayed M Tawfeek3

  • 1Computer Science Department, Faculty of Computers and Information, Suez University, Suez 43533, Egypt.

Diagnostics (Basel, Switzerland)
|November 24, 2023
PubMed
概括

这项研究引入了一种新的hyOPTGB模型,用于预测埃及的C型肝炎病毒 (HCV) 感染,达到95.3%的准确性. 该模型优化了梯度增强,以更好地预测疾病和公共卫生洞察力.

关键词:
这就是OPTUNA OPTUNA.梯度增强 (GB) 是一个型肝炎病毒 (HCV) 病毒这就是超参数的超参数.优化的优化优化优化.

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科学领域:

  • 医疗信息学 医疗信息学
  • 机器学习 机器学习
  • 流行病学 流行病学

背景情况:

  • 埃及面临着C型肝炎病毒 (HCV) 感染的高患病率,原因包括注射药物使用和医疗保健实践不足等因素.
  • 准确预测HCV对于有效的公共卫生干预和在高发病率地区的资源分配至关重要.

研究的目的:

  • 开发和评估一个高度准确的机器学习模型,用于预测埃及的HCV感染.
  • 使用相关数据集,将拟议的hyOPTGB模型的性能与其他既有机器学习算法进行比较.

主要方法:

  • 开发了一个新的hyOPTGB模型,利用一个优化的梯度增强分类器,通过OPTUNA框架进行超参数调整.
  • 数据预处理涉及使用前选择 (FS) 包装方法进行Min-Max规范化和特征选择.
  • 该模型在来自UCI机器学习库的1385个实例和29个特征的数据集上进行了训练和评估.

主要成果:

  • 该hyOPTGB模型实现了95.3%的卓越精度,优于其他模型,包括决策树 (DT),支向量机 (SVM),模拟分类器 (DC),分类器 (RC) 和包装分类器 (BC).
  • 通过对同一个数据集应用的现有模型进行比较,进一步验证了性能,证明了一致的有效性.
  • 使用关键性能指标,如准确性,回忆,精度和F1得分来评估系统的有效性.

结论:

  • 在埃及,hyOPTGB模型显示出作为预测HCV感染的有效工具的巨大潜力.
  • 优化梯度增强方法为改善公共卫生诊断准确性提供了一个有希望的方向.
  • 准确的HCV预测可以支持有针对性的干预措施,并减少高患病率人群中的疾病负担.