Citation

Neoh, Hoo Thye and Chan, Zheng Uee and Ng, Wei Da and Ng, Kok Why (2026) Predicting Marital Satisfaction with Machine Learning: A Maslow-Informed, Cross-Cultural Study. JOIV : International Journal on Informatics Visualization, 10 (4). p. 1470. ISSN 2549-9610

Text 4753-16445-1-PB.pdf - Published Version Restricted to Repository staff only Download (3MB)

Abstract

Marital satisfaction is one of the major determinants of personal well-being and family stability, but the predictors are still complicated in relation to cultural contexts. This paper aims to identify and model the variables that influence marital satisfaction using machine-learning-based predictive modeling and Maslow's needs. The preprocessing of a global survey dataset involved variable mapping, duplicate removal, type correction, and range reversal to ensure consistency. Exploratory and correlational analyses showed that marital satisfaction was strongly correlated with psychosocial variables, including love, safety, and self-actualization. Feature selection (Recursive Feature Elimination) found six important predictors: age, length of marriage, number of children raised, religiosity, safety, and love. It was trained on five machine learning models, the best test accuracy of which was 86.84% with the ensemble method Gradient Boosting using a binary classification scheme, with repeated hold-out validation and statistical testing used to assess evaluation robustness. Although there were challenges due to class imbalance, binary grouping achieved better predictive performance than multiclass classification. These results indicate that methods integrating psychological frameworks and machine learning may yield valid, interpretable models of marital satisfaction, with potential applications in relationship counseling, cross-cultural studies, and educational programs.

Item Type:	Article
Uncontrolled Keywords:	Marital satisfaction; machine learning; predictive modeling; Maslow needs; feature selection; ensemble method; gradient boosting; class imbalance
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	02 Oct 2026 02:37
Last Modified:	02 Oct 2026 02:37
URII:	http://shdl.mmu.edu.my/id/eprint/16825

Downloads

Edit (login required)