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. 2024 Sep 6;14(1):20811.
doi: 10.1038/s41598-024-71854-w.

Prediction of childbearing tendency in women on the verge of marriage using machine learning techniques

Khadijeh Moulaei  1   2 , Mohammad Mahboubi  3 , Sasan Ghorbani Kalkhajeh  3   4 , Hadi Kazemi-Arpanahi  5
Affiliations

Prediction of childbearing tendency in women on the verge of marriage using machine learning techniques

Khadijeh Moulaei et al. Sci Rep. .

Abstract

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.

Keywords: Childbearing tendency; Fertility; Forecasting; Machine learning; Marriage; Reproductive behavior.

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Conflict of interest statement

The authors declare no competing interests.

The design and performance of our study are described and justified in a research protocol. The protocol includes information regarding funding, sponsors (funded), institutional affiliations, potential conflicts of interest, incentives for subjects, and information regarding provisions for treating and/or compensating subjects who are harmed as a consequence of participation in the research study. This protocol is as follows:

Figures

Fig. 1
Fig. 1
Performance evaluation of selected algorithms.
Fig. 2
Fig. 2
ROC curves comparing ML algorithms for prediction of childbearing tendency in women on the verge of marriage.
See this image and copyright information in PMC

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