基于机器学习的数学方程用于使用基本实验室参数检测登革热阳性
Shirin Dasgupta1, Shuvankar Das2, Debarghya Chakraborty2
1Dr. B. C. Roy Multi Speciality Medical Research Centre, Indian Institute of Technology Kharagpur, West Bengal, India.
Journal of family medicine and primary care
|May 21, 2025
概括
机器学习模型使用基本的患者数据预测登革热感染,为传统诊断提供了具有成本效益的替代方案. 人工神经网络模型实现了95.83%的准确性,突出显示了血小板计数作为关键指标.
科学领域:
- 医学诊断 医学诊断 医学诊断
- 计算生物学 计算生物学
- 流行病学 流行病学
背景情况:
- 登革热是一种具有全球公共卫生影响的重大树状病毒疾病,需要对严重病例进行早期检测.
- 传统的登革热诊断方法 (ELISA,RT-PCR) 在资源有限的环境中往往无法使用.
- 机器学习为可访问和负担得起的登革热诊断提供了一个潜在的解决方案.
研究的目的:
- 评估多变量自适应回归脊柱 (MARS) 和人工神经网络 (ANN) 模型在预测登革热感染方面的有效性.
- 确定登革热诊断的关键临床参数.
- 为登革热开发可访问的诊断工具.
主要方法:
- 利用MARS和ANN机器学习模型来预测登革热感染.
- 输入参数包括年龄,全白细胞计数 (TLC),血红蛋白,血小板计数和红细胞沉率 (ESR).
- 在印度Midnapore诊断中心测试的122名患者的数据上评估模型.
主要成果:
- ANN模型实现了95.83%的预测准确度,而MARS模型实现了87.5%.
- 血小板计数被确定为两种模型中登革热阳性最重要的预测因素.
- 该研究提出了用于登革热阳性检测的两个预测数学方程.
结论:
- 机器学习模型,特别是ANN,提供准确和可访问的登革热感染预测.
- 简单的临床参数,如血小板计数,对于早期检测登革热至关重要.
- 这些ML模型可以在资源有限的环境中作为登革热诊断的有价值的工具.
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