[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4606":3,"related-4606":74},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"primary_topic":9,"secondary_topics":21,"classification":22,"search_phrases":39,"slug":40,"view_count":15,"doi":41,"paper":42,"created_at":73},4606,"Development and validation of an explainable machine learning-based risk prediction model for macrosomia in han Chinese women with singleton cephalic pregnancies","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-65628-9","To develop and internally validate an explainable machine learning model to predict macrosomia risk among Han Chinese women with singleton cephalic pregnancies during late gestation or at admission. Data from 3,233 women who delivered at a tertiary hospital from April 2024 to March 2025 were included (158 macrosomia cases). All candidate variables were derived from objective clinical data, including electronic medical records, admission measurements, and prenatal ultrasound examinations. To avoid data leakage, the dataset was initially partitioned using an 8:2 stratified split into training and held-out test sets. Synthetic Minority Over-sampling Technique (SMOTE) was applied exclusively within the training set and cross-validation training folds to address class imbalance. A complete analytical pipeline incorporating median imputation, appropriate feature scaling, and SMOTE was implemented. Multiple models were constructed and compared, including logistic regression (LR), random forest (RF), extreme gradient boosting (XGBoost), CatBoost, k-nearest neighbors (KNN), and multi-layer perceptron (MLP). Mean stratified cross-validation ROC-AUC on the training set served as the primary criterion for model selection, supplemented by the Brier score, calibration curves, and decision curve analysis (DCA) to evaluate model reliability and clinical net benefit. LR achieved the highest cross-validated ROC-AUC on the training set, yielding an AUC of 0.934 (95% CI: 0.906–0.957), sensitivity of 0.833 (95% CI: 0.727–0.923), specificity of 0.876 (95% CI: 0.856–0.907), precision of 0.248 (95% CI: 0.184–0.322), and F1 score of 0.382 (95% CI: 0.295–0.458) on the held-out test set. Among the machine learning models, CatBoost exhibited relatively balanced performance regarding AUC, calibration, and clinical interpretability. Although XGBoost demonstrated the highest accuracy, its sensitivity was low, and it was thus not selected as the best model based solely on mean accuracy, precision, and recall. SHapley Additive exPlanations (SHAP) analysis indicated that fetal abdominal circumference (AC), fundal height, maternal height, gestational weeks at delivery, and maternal AC at admission contributed most significantly to prediction. This study established a reproducible and interpretable macrosomia risk prediction pipeline tailored for late-pregnancy\u002Fadmission decision support. The model provides supplementary information for managing delivery and care in high-risk pregnancies; however, its widespread clinical application requires large-scale, multicenter, and prospective external validation.","开发并内部验证一个可解释的机器学习模型，用于预测汉族单胎头位妊娠女性在妊娠晚期或入院时发生巨大儿（macrosomia）的风险。纳入2024年4月至2025年3月在一家三级医院分娩的3,233名女性的数据（158例巨大儿）。所有候选变量均来源于客观临床数据，包括电子病历、入院测量值和产前超声检查。为避免数据泄漏，数据集首先按8：2分层划分分为训练集和留出测试集。合成少数类过采样技术（Synthetic Minority Over-sampling Technique, SMOTE）仅在训练集和交叉验证训练折中应用，以处理类别不平衡。实施了包含中位数插补、适当特征缩放和SMOTE的完整分析流程。构建并比较了多种模型，包括逻辑回归（logistic regression, LR）、随机森林（random forest, RF）、极端梯度提升（extreme gradient boosting, XGBoost）、CatBoost、k近邻（k-nearest neighbors, KNN）和多层感知机（multi-layer perceptron, MLP）。以训练集上的平均分层交叉验证ROC-AUC作为模型选择的主要标准，并辅以Brier评分、校准曲线和决策曲线分析（decision curve analysis, DCA）来评估模型可靠性和临床净获益。LR在训练集上获得了最高的交叉验证ROC-AUC，在留出测试集上的AUC为0.934（95% CI：0.906–0.957），灵敏度为0.833（95% CI：0.727–0.923），特异度为0.876（95% CI：0.856–0.907），精确率为0.248（95% CI：0.184–0.322），F1分数为0.382（95% CI：0.295–0.458）。在机器学习模型中，CatBoost在AUC、校准和临床可解释性方面表现出相对均衡的性能。尽管XGBoost表现出最高的准确率，但其灵敏度较低，因此不能仅基于平均准确率、精确率和召回率将其选为最佳模型。SHapley加法解释（SHapley Additive exPlanations, SHAP）分析表明，胎儿腹围（abdominal circumference, AC）、宫高、母亲身高、分娩时孕周和入院时母亲AC对预测的贡献最为显著。本研究建立了一个可重复且可解释的巨大儿风险预测流程，适用于妊娠晚期\u002F入院时的决策支持。该模型为高危妊娠的分娩管理和护理提供了补充信息；然",null,"Scientific Reports","2026-10-08T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该文为汉族单胎头位妊娠巨大儿风险的机器学习预测模型研究，属临床医学领域，与三农、农业信息化、智慧农业等主题无关。",[19],{"name":10,"url":6},[12],[],{"status":23,"locked":14,"version":24,"method":25,"evidence":26},"needs_review","2026-10-22.2","rules",{"candidates":27,"agricultural_evidence":14,"reason":38},[28],{"code":29,"score":30,"supported":31,"title":32,"abstract":34,"tags":37},"I2",17,true,[33],"machine learning",[35,33,36],"机器学习","decision support",[],"农业相关性需要复核",[],"Developmentandvalidationofanexplainablem-4606","10.1038\u002Fs41598-026-65628-9",{"doi":41,"openalex_id":43,"authors":44,"venue":10,"cited_by_count":15,"oa_url":65,"card":66,"direction":70,"ingested_from":72},"W7220933183",[45,47,49,51,53,55,57,59,61,63],{"name":46,"orcid":9},"Jiao Wang",{"name":48,"orcid":9},"Xianmin Wang",{"name":50,"orcid":9},"Yanmin Yu",{"name":52,"orcid":9},"Wei Xiang",{"name":54,"orcid":9},"Ying Li",{"name":56,"orcid":9},"XiaoFeng Zhang",{"name":58,"orcid":9},"Qian Sun",{"name":60,"orcid":9},"Yu He",{"name":62,"orcid":9},"Jianqun Li",{"name":64,"orcid":9},"Hongyu Yuan","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-65628-9_reference.pdf",{"tldr":67,"method":68,"finding":69,"direction":70,"opportunity":71},"构建可解释机器学习模型预测汉族单胎头位孕妇巨大儿风险并内部验证。","3233例临床数据，SMOTE平衡，比较LR\u002FRF\u002FXGBoost等，SHAP解","LR交叉验证AUC最高0.934，CatBoost综合较均衡，关键特征为胎儿腹围等。","农业人工智能与决策模型","可迁移至农业领域构建可解释风险预测模型，如作物病害或畜产品质预测。","openalex","2026-10-10T23:30:47.940592Z",{"total":15,"page":75,"page_size":75,"items":76},1,[]]