Wongveerapaiboon P.Adsavakulchai S.Soonsawad P.Cheng R.H.Mahidol University2025-09-132025-09-132025-01-01IEEE International Conference on Electro Information Technology (2025) , 47-5021540357https://repository.li.mahidol.ac.th/handle/123456789/112039This 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.Computer ScienceEngineeringConference PaperSCOPUS10.1109/eIT64391.2025.111036302-s2.0-10501491062521540373