Taeidi Elham, Ranjbar Amene, Montazeri Farideh, Mehrnoush Vahid, Darsareh Fatemeh
Mother and Child Welfare Research Center, Hormozgan University of Medical Sciences, Bandar Abbas, IRN.
Fertility and Infertility Research Center, Hormozgan University of Medical Sciences, Bandar Abbas, IRN.
Cureus. 2023 Jul 6;15(7):e41448. doi: 10.7759/cureus.41448. eCollection 2023 Jul.
Creating a prediction model incorporating multiple risk factors for intrauterine growth restriction is vital. The current study employed a machine learning model to predict intrauterine growth restriction.
This cross-sectional study was carried out in a tertiary hospital in Bandar Abbas, Iran, from January 2020 to January 2022. Women with singleton pregnancies above the gestational age of 24 weeks who gave birth during the study period were included. Exclusion criteria included multiple pregnancies and fetal anomalies. Four statistical learning algorithms were used to build a predictive model: (1) Decision Tree Classification, (2) Random Forest Classification, (3) Deep Learning, and (4) the Gradient Boost Algorithm. The candidate predictors of intrauterine growth restriction for all models were chosen based on expert opinion and prior observational cohorts. To investigate the performance of each algorithm, some parameters, including the area under the receiver operating characteristic curve (AUROC), accuracy, precision, and sensitivity, were assessed.
Of 8683 women who gave birth during the study period, 712 were recorded as having intrauterine growth restriction, with a frequency of 8.19%. Comparing the performance parameters of different machine learning algorithms showed that among all four machine learning models, Deep Learning had the greatest performance to predict intrauterine growth restriction with an AUROC of 0.91 (95% confidence interval, 0.85-0.97). The importance of the variables revealed that drug addiction, previous history of intrauterine growth restriction, chronic hypertension, preeclampsia, maternal anemia, and COVID-19 were weighted factors in predicting intrauterine growth restriction.
A machine learning model can be used to predict intrauterine growth restriction. The Deep Learning model is an accurate algorithm for predicting intrauterine growth restriction.
创建一个包含多种宫内生长受限风险因素的预测模型至关重要。本研究采用机器学习模型来预测宫内生长受限。
本横断面研究于2020年1月至2022年1月在伊朗阿巴斯港的一家三级医院进行。纳入研究期间分娩的孕龄超过24周的单胎妊娠妇女。排除标准包括多胎妊娠和胎儿畸形。使用四种统计学习算法构建预测模型：（1）决策树分类法，（2）随机森林分类法，（3）深度学习法，以及（4）梯度提升算法。所有模型的宫内生长受限候选预测因素均根据专家意见和先前的观察队列选定。为了研究每种算法的性能，评估了一些参数，包括受试者工作特征曲线下面积（AUROC）、准确性、精确性和敏感性。
在研究期间分娩的8683名妇女中，有712名被记录为患有宫内生长受限，发生率为8.19%。比较不同机器学习算法的性能参数表明，在所有四种机器学习模型中，深度学习法预测宫内生长受限的性能最佳，AUROC为0.91（95%置信区间，0.85 - 0.97）。变量的重要性显示，药物成瘾、既往宫内生长受限史、慢性高血压、先兆子痫、母体贫血和新冠病毒病是预测宫内生长受限的加权因素。
机器学习模型可用于预测宫内生长受限。深度学习模型是预测宫内生长受限的一种准确算法。
