Department of Health Information Technology, Faculty of Paramedical, Ilam University of Medical Sciences, Ilam, Iran.
Artificial Intelligence in Medical Sciences Research Center, Smart University of Medical Sciences, Tehran, Iran.
Sci Rep. 2024 Sep 6;14(1):20811. doi: 10.1038/s41598-024-71854-w.
The declining fertility rate and increasing marriage age among girls pose challenges for policymakers, leading to issues such as population decline, higher social and economic costs, and reduced labor productivity. Using machine learning (ML) techniques to predict the desire to have children can offer a promising solution to address these challenges. Therefore, this study aimed to predict the childbearing tendency in women on the verge of marriage using ML techniques. Data from 252 participants (203 expressing a "desire to have children" and 49 indicating "reluctance to have children") in Abadan, and Khorramshahr cities (Khuzestan Province, Iran) was analyzed. Seven ML algorithms, including multilayer perceptron (MLP), support vector machine (SVM), logistic regression (LR), random forest (RF), J48 decision tree, Naive Bayes (NB), and K-nearest neighbors (KNN), were employed. The performance of these algorithms was assessed using metrics derived from the confusion matrix. The RF algorithm showed superior performance, with the highest sensitivity (99.5%), specificity (95.6%), and receiver operating characteristic curve (90.1%) values. Meanwhile, MLP emerged as the top-performing algorithm, showcasing the best overall performance in accuracy (77.75%) and precision (81.8%) compared to other algorithms. Factors such as age of marriage, place of residence, and strength of the family center with the birth of a child were the most effective predictors of a woman's desire to have children. Conversely, the number of daughters, the wife's ethnicity, and the spouse's ownership of assets such as cars and houses were among the least important factors in predicting this desire. ML algorithms exhibit excellent predictive capabilities for childbearing tendencies in women on the verge of marriage, highlighting their remarkable effectiveness. This capacity to offer accurate prognoses holds significant promise for advancing research in this field.
生育率下降和女孩初婚年龄上升给政策制定者带来了挑战，导致人口减少、社会和经济成本增加以及劳动生产率降低等问题。使用机器学习 (ML) 技术预测生育意愿可以为解决这些挑战提供一个有前途的解决方案。因此，本研究旨在使用 ML 技术预测即将结婚的女性的生育倾向。分析了来自阿巴丹和霍拉姆沙赫尔市（伊朗胡齐斯坦省）的 252 名参与者（203 名表示“有生育意愿”和 49 名表示“不愿生育”）的数据。采用了包括多层感知器 (MLP)、支持向量机 (SVM)、逻辑回归 (LR)、随机森林 (RF)、J48 决策树、朴素贝叶斯 (NB) 和 K-最近邻 (KNN) 在内的 7 种 ML 算法。使用混淆矩阵得出的指标评估这些算法的性能。RF 算法表现出优异的性能，具有最高的敏感性（99.5%）、特异性（95.6%）和接收者操作特征曲线（90.1%）值。同时，MLP 作为表现最佳的算法脱颖而出，在准确性（77.75%）和精度（81.8%）方面均优于其他算法，表现出最佳的整体性能。婚姻年龄、居住地点以及家庭中心对孩子出生的强度等因素是女性生育意愿的最有效预测因素。相反，女儿的数量、妻子的种族以及配偶拥有汽车和房屋等资产的情况是预测这种意愿的最不重要因素之一。ML 算法对即将结婚的女性生育倾向具有出色的预测能力，突出了其卓越的有效性。这种提供准确预测的能力为该领域的研究提供了重要的推动作用。
