Citation

Sponge, Khye and Ng, Kok Why and Ting, Choo Yee and Chai, Ian (2025) Cross-cultural prediction of marital satisfaction using machine learning algorithms and generic needs. Bulletin of Electrical Engineering and Informatics, 14. pp. 3622-3631. ISSN 2089-3191

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Abstract

Marital satisfaction is crucial for individual well-being and family stability. Prior research has predominantly focused on Western contexts using traditional statistical models, limiting the generalizability of findings across cultures. This study addresses a significant gap by employing machine learning algorithms Naive Bayes, support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) on a diverse dataset comprising responses from 7,178 participants across 33 countries. Our methodology includes a robust data preprocessing pipeline, feature selection, and algorithm evaluation, emphasizing their practical application in relationship interventions. Using predictors derived from Maslow's generic needs, including love, respect, and pride in one's spouse, we demonstrate that these factors are significant cross-cultural predictors of marital satisfaction. Our results show that pride in spouse, love, and respect for spouse are the most significant predictors of marital satisfaction across cultures. This demonstrates the effectiveness of machine learning in capturing complex relationships, offering more accurate predictions than traditional methods. These findings suggest that fostering love, respect, and sacrifice in early relationships can significantly enhance marital satisfaction across diverse cultural contexts.

Item Type:	Article
Uncontrolled Keywords:	Machine learning algorithms, sustainable development goals
Subjects:	H Social Sciences > HQ The family. Marriage. Woman Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Nor Afiqah Mohd Adnan
Date Deposited:	06 Nov 2025 07:28
Last Modified:	07 Nov 2025 01:48
URII:	http://shdl.mmu.edu.my/id/eprint/14735

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