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使用机器学习来预测中国妇女患有多囊性卵巢疾病的患者

Chen-Yu Wang¹, Dee Pei², Chun-Kai Wang³

¹Department of Obstetrics and Gynecology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan; Graduate Institute of Applied Science and Engineering, Fu Jen Catholic University, New Taipei City, Taiwan.

Taiwanese journal of obstetrics & gynecology

|January 10, 2025

概括

与逻辑回归相比,机器学习 (Mach-L) 模型在预测多囊性卵巢综合征 (PCOS) 中表现出更高的准确性. 年龄,甘油三和GPT被确定为PCOS的关键风险因素.

科学领域:

内分泌学在内分泌学.
医疗信息学医疗信息学
数据科学数据科学数据科学

背景情况:

多囊卵巢综合征 (PCOS) 影响全球5-21%的患者,使其成为一种普遍的荷尔蒙障碍.
传统的物流回归 (LR) 在分析复杂的PCOS相关因素方面存在局限性.
机器学习 (Mach-L) 为医学研究提供了先进的分析能力.

研究的目的:

为了比较五个马赫-L算法的预测准确度与PCOS的传统LR.
使用Mach-L来预测PCOS发病率并确定重大风险因素.

主要方法:

分析了170名PCOS患者和950名对照组的队列.
收集了人口,生化和生活方式数据,通过鹿特丹标准诊断PCOS.
使用了五种Mach-L算法 (RF,SGB,MARS,XGBoost,CatBoost),并与LR进行了比较.

主要成果:

马赫-L方法的估计误差较小,平均AUC (0.6669) 比LR (0.5908) 高.
PCOS患者年轻,GOT,GPT,γ-GT,TG和教育程度较高.
通过Mach-L确定的主要风险因素包括年龄,TG,GPT,WBC,UA和Plt.

结论:

马赫-L技术在预测中国队列中PCOS的表现优于传统的LR.
年龄是最重要的预测因素,其次是TG,GPT,WBC,UA和Plt.

关键词:

后勤回归的逻辑回归机器学习机器学习多囊性卵巢综合征多囊性卵巢综合征

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