浙江医学2025，Vol.47Issue(20)：2142-2147,6.DOI:10.12056/j.issn.1006-2785.2025.47.20.2024-2670
男性不育列线图风险预测模型的构建与验证
Construction and validation of a nomogram risk prediction model for male infertility
摘要
Abstract
Objective To develop and validate a nomogram risk prediction model for male infertility based on machine learning algorithms.Methods A retrospective study was conducted on 124 male infertility patients(infertility group)and 31 normozoospermic males(control group)from Hangzhou Traditional Chinese Medicine Hospital between January and December 2023.Data on blood routine,biochemical profiles,reproductive hormones,vitamins,and reproductive antibodies were collected and compared between the groups.Statistically significant variables were incorporated into least absolute shrinkage and selection operator(LASSO)regression to identify predictors using the minimum λ value.The dataset was randomly split into a training set(n=107,70％)and a validation set(n=48,30％).Five prediction models were built using decision tree(DT),K-nearest neighbor(KNN),logistic regression(LR),random forest(RF),and support vector machine(SVM)algorithms.The best-performing algorithm was used to construct the nomogram.Results Thirteen variables were included in the LASSO regression,and 10 predictors were selected with the minimum λ(λ=0.010 4).Among the five prediction models,the LR model demonstrated the best performance(AUC=0.942,accuracy=0.879,sensitivity=0.903,specificity=0.748).Multivariate logistic regression analysis on the 10 predictors identified five final predictors:progressive motility,total sperm count,vitamin B1,vitamin B3,and 25-hydroxyvitamin D.The nomogram constructed with these factors achieved an AUC of 0.893 and accuracy of 0.842 in predicting male infertility.Calibration curve analysis indicated good agreement between predicted and actual probabilities.Conclusion This study successfully constructed and validated prediction models for male infertility using machine learning algorithms.The developed nomogram risk prediction model effectively assesses the risk of male infertility and shows potential as a reference tool for early screening.关键词
不育/男性/机器学习/列线图/预测模型Key words
Infertility/Male/Machine learning/Nomogram/Prediction model引用本文复制引用
吴瑾,刘哲琪,罗仙忠,李昊颖,赵宏利..男性不育列线图风险预测模型的构建与验证[J].浙江医学,2025,47(20):2142-2147,6.基金项目
浙江省中医药科学研究基金资助项目(2022ZB235) （2022ZB235）
