Machine Learning Approaches to Predict Divorce-Related Single Motherhood
1
Issued Date
2025-01-01
Resource Type
ISSN
21540357
eISSN
21540373
Scopus ID
2-s2.0-105014910625
Journal Title
IEEE International Conference on Electro Information Technology
Start Page
47
End Page
50
Rights Holder(s)
SCOPUS
Bibliographic Citation
IEEE International Conference on Electro Information Technology (2025) , 47-50
Suggested Citation
Wongveerapaiboon P., Adsavakulchai S., Soonsawad P., Cheng R.H. Machine Learning Approaches to Predict Divorce-Related Single Motherhood. IEEE International Conference on Electro Information Technology (2025) , 47-50. 50. doi:10.1109/eIT64391.2025.11103630 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/112039
Title
Machine Learning Approaches to Predict Divorce-Related Single Motherhood
Corresponding Author(s)
Other Contributor(s)
Abstract
This study aims to apply machine learning for determining the factors that may lead to divorce in single motherhood in Thailand. The research is based on the Cross-Industry Standard Process (CRISP) that consists of six steps from problem understanding to deployment using Python as a tool. Thailand's National Statistical Office showed the decline in marriage registration rates since 2004 while divorce rates raised. A questionnaire was designed to collect a sample of 38 female participants specified aged>18 years with a minimum of one child. The dataset was partitioned using a 75:25 ratio for training and testing purposes. Three machine learning algorithms were implemented: Random Forest, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The results showed that KNN and SVM algorithms predict divorce leading to single motherhood with 100% accuracy. This preliminary study provides a methodological framework for investigating divorce prediction using machine learning, however the more comprehensive research incorporating sociocultural dimensions specific to Thailand.