Exploring machine learning algorithms for predicting fertility preferences among reproductive age women in Nigeria.

Abstract

Background: Fertility preferences refer to the number of children an individual would like to have, regardless of any obstacles that may stand in the way of fulfilling their aspirations. Despite the creation and application of numerous interventions, the overall fertility rate in West African nations, particularly Nigeria, is still high at 5.3% according to 2018 Nigeria Demographic and Health Survey data. Hence, this study aimed to predict the fertility preferences of reproductive age women in Nigeria using state-of-the-art machine learning techniques.

Methods: Secondary data analysis from the recent 2018 Nigeria Demographic and Health Survey dataset was employed using feature selection to identify predictors to build machine learning models. Data was thoroughly assessed for missingness and weighted to draw valid inferences. Six machine learning algorithms, namely, Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest, and eXtreme Gradient Boosting, were employed on a total sample size of 37,581 in Python 3.9 version. Model performance was assessed using accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUROC). Permutation and Gini techniques were used to identify the feature's importance.

Results: Random Forest achieved the highest performance with an accuracy of 92%, precision of 94%, recall of 91%, F1-score of 92%, and AUROC of 92%. Factors influencing fertility preferences were number of children, age group, and ideal family size. Region, contraception intention, ethnicity, and spousal occupation had a moderate influence. The woman's occupation, education, and marital status had a lower impact.

Conclusion: This study highlights the potential of machine learning for analyzing complex demographic data, revealing hidden factors associated with fertility preferences among Nigerian women. In conclusion, these findings can inform more effective family planning interventions, promoting sustainable development across Nigeria.