Machine learning approaches to modelling marriage duration in South Africa

Abstract

Marriage is one of the most important social institutions, as it greatly influences the stability of families, population, and socio-economic well-being. The changing trends of marriages and divorces in South Africa confirm the importance of predictive analysis on the stability of marriages. Marriage duration prediction is a challenging task, as it is affected by a number of factors. Recent developments in machine learning techniques provide new opportunities to model this problem. This experiment used the following seven different classifications: Random Forest, Gradient Boosting, XGBoost, Logistic Regression, Support Vector Machine, K Nearest Neighbours, and Decision Tree, which aimed to forecast the categories of marriage length (short, medium, and long). The models used the following metrics: accuracy, precision, recall, F1-score, macro ROC-AUC, and the ROC curve. The results showed that ensemble-based approaches performed better than conventional classifiers. Gradient Boosting had the best performance in macro ROC AUC and was best at discriminating classes, especially for long and medium-length marriages. Random Forest had the best overall accuracy and was best at balancing classes. XGBoost had a similar performance to that of Random Forest in predicting. SVM had a high precision value but a lower recall value. Decision Trees and KNN had poor performance. Gradient Boosting was found to be the best-performing method for multi-class prediction of duration of marriage and achieved a balance between sensitivity and specificity. The results obtained from these studies prove that ensemble learning methods can be effectively utilized for modelling complex societal events.

Keywords: {'name': 'Machine learning; Marriage duration; Classification models; Gradient boosting; Random Forest; Predictive modelling'}

Authors & affiliations

Muhammad Hoque
Sefako Makgatho Health Sciences University

Rafiul Hoque
Universiti Tenaga Nasional

Deposited references

Supplied references, not a list of publications citing this article.

DOI: 10.3389/fpsyg.2020.00154

DOI: 10.1023/A:1010933404324

DOI: 10.1145/2939672.2939785

DOI: 10.1023/A:1022627411411

DOI: 10.1109/TIT.1967.1053964

DHS Program. (2019). South Africa demographic and health survey 2016 (SADHS 2016). DHS Program.

DOI: 10.1214/aos/1013203451

DOI: 10.1186/s12978-022-01339-4

DOI: 10.1007/978-0-387-84858-7

DOI: 10.1038/s41598-024-72790-5

Komane, M. M. (2024). Application of survival analysis and machine learning models (Master’s thesis, University of Limpopo).

Mathabatha, S. M. L. (2023). Multilevel modelling of the determinants of early marriage among women aged 20–49 years in South Africa. African Journal of Reproductive Health, 27(2), 1–10.

Mencarin, M. (2025). Analyzing determinants of marriage survival by random survival forests. Demographic Research, 52, 1–20.

DOI: 10.24042/djm.v5i3.13742

DOI: 10.1063/5.0111322

Sani, J. (2025). Application of machine learning algorithms and SHAP to identify predictors of fertility preferences. BMC Public Health, 25(1), 1227.

Statistics South Africa. (2023). Marriages and divorces, 2022 (Statistical Release P0307). Statistics South Africa. https://www.statssa.gov.za/publications/P0307/P03072022.pdf