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在东亚人群中基于机器学习的预测第一季度唐氏综合征风险

Yen-Tin Chen^1,2, Gina Jinna Chen³, Yu-Shiang Lin¹

¹In-Service Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.

Risk management and healthcare policy

|April 3, 2025

概括

机器学习模型准确地预测了东亚人口的第一季度唐氏综合征风险. 人工神经网络 (ANN) 结合随机过量采样 (ROS) 实现了最高的AUC 0.939,提高了查效率.

科学领域:

遗传学和基因组学遗传学和基因组学
医疗保健中的人工智能
孕产妇和胎儿医学孕产妇和胎儿医学

背景情况:

唐氏综合征是新生儿中最常见的染色体异常.
它经常导致发育迟缓和先天性结构异常.
准确的早期预测对于及时干预和管理至关重要.

研究的目的:

确定一个最佳的机器学习模型来预测第一季度唐氏综合征风险.
提高东亚人口中唐氏综合征查的准确性和效率.
探索各种机器学习模型和数据平衡算法的预测能力.

主要方法:

利用了来自台北长光纪念医院的3812例病例数据集 (2018年5月~2024年2月).
在12个机器学习模型中输入14个特征,包括母亲年龄和血清标记.
采用了七个数据平衡算法,并使用AUC,准确性,精度,回忆和F1分数来评估模型性能.

主要成果:

在高风险病例中,LightGBM与随机低样本 (RUS) 实现了最高的召回率 (0.84).
人工神经网络 (ANN) 和长期短期记忆 (LSTM) 模型与随机过量采样 (ROS) 结合,达到0.939.9的曲线下面面积 (AUC) 最高.
该ANN-ROS模型在分类唐氏综合征风险方面表现出卓越的表现.

结论:

拟议的ANN模型利用深度神经网络和ROS,实现AUC为0.939和准确度为0.97.
这种模型显示出在东亚人口中准确的第一季度唐氏综合征风险预测的巨大潜力.
这些发现表明,在唐氏综合征查过程中,提高了效率和准确性.

关键词:

深度神经网络是一个神经网络.第一个三个月的唐氏综合征查.机器学习是机器学习.

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