Scholarlink

<p>Accurate prediction of fertility behavior is essential for sustainable population management and effective policy planning. This study applies advanced machine learning (ML) techniques to predict Total Children Ever Born (TCEB) using data from the Pakistan Demographic and Health Survey (PDHS). After data screening, observations and socio-demographic variables were retained for model development. Multiple models, including Poisson Regression, Random Forest (RF), Support Vector Regression (SVR), Decision Tree (DT), Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and XG-Boost, were trained and evaluated using standard regression metrics (MSE, RMSE, MAE, and R²). Hyper-parameter tuning and cross-validation were employed to enhance model robustness, while 20% of the data was reserved for independent testing. Results indicate that ensemble-based models, particularly XG-Boost, consistently outperform conventional approaches across training, validation, and test datasets, achieving the lowest prediction errors and highest explanatory power. Feature importance analysis highlights the dominant influence of family composition, age, and fertility preferences on reproductive outcomes. The findings demonstrate that advanced ML frameworks can effectively capture complex and non-linear fertility patterns, providing reliable tools for demographic forecasting and evidence-based policy formulation. This study contributes to fertility research by integrating optimized ML models with large-scale survey data to support sustainable population and health planning in Pakistan and similar socio-economic contexts.</p> Graphical Abstract <p></p> <p>The graphical abstract illustrates the application of machine learning to predict fertility outcomes in Pakistan, based on data from the Pakistan Demographic and Health Survey (PDHS). Among the models evaluated, the optimized Random Forest algorithm achieved the highest predictive accuracy for estimating the Total Children Ever Born (TCEB). Feature importance analysis identified family composition and demographic factors as the most influential predictors, with the top-ranked feature demonstrating substantially higher importance than others. These findings highlight the value of machine learning in demographic research and its potential to support evidence-based policy planning.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Data-Driven Prediction of Fertility Outcomes Through Machine Learning Models

  • 期刊论文

  • 发表日期：

    2026年3月3日

查看全文
  • Alamgir,
  • Abdur Rehman,
  • Ubaidullah,
  • Rizwan Niaz,
  • Qasim Shah,
  • Riaz Ali,
  • Arfan Arshad,
  • Mansour Almazroui

摘要

Accurate prediction of fertility behavior is essential for sustainable population management and effective policy planning. This study applies advanced machine learning (ML) techniques to predict Total Children Ever Born (TCEB) using data from the Pakistan Demographic and Health Survey (PDHS). After data screening, observations and socio-demographic variables were retained for model development. Multiple models, including Poisson Regression, Random Forest (RF), Support Vector Regression (SVR), Decision Tree (DT), Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and XG-Boost, were trained and evaluated using standard regression metrics (MSE, RMSE, MAE, and R²). Hyper-parameter tuning and cross-validation were employed to enhance model robustness, while 20% of the data was reserved for independent testing. Results indicate that ensemble-based models, particularly XG-Boost, consistently outperform conventional approaches across training, validation, and test datasets, achieving the lowest prediction errors and highest explanatory power. Feature importance analysis highlights the dominant influence of family composition, age, and fertility preferences on reproductive outcomes. The findings demonstrate that advanced ML frameworks can effectively capture complex and non-linear fertility patterns, providing reliable tools for demographic forecasting and evidence-based policy formulation. This study contributes to fertility research by integrating optimized ML models with large-scale survey data to support sustainable population and health planning in Pakistan and similar socio-economic contexts.

Graphical Abstract

The graphical abstract illustrates the application of machine learning to predict fertility outcomes in Pakistan, based on data from the Pakistan Demographic and Health Survey (PDHS). Among the models evaluated, the optimized Random Forest algorithm achieved the highest predictive accuracy for estimating the Total Children Ever Born (TCEB). Feature importance analysis identified family composition and demographic factors as the most influential predictors, with the top-ranked feature demonstrating substantially higher importance than others. These findings highlight the value of machine learning in demographic research and its potential to support evidence-based policy planning.