International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

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 Volume 13, Issue 9 (September 2026), Pages: 130-142

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 Original Research Paper

Applying decision tree models to identify key factors influencing voluntary military enlistment: A case study of Thailand's 23rd Military District

 Author(s): 

Thotsaporn Phanngoen, Kriangsak Chanthinok *, Palan Jantarajaturapath

 Affiliation(s):

Department of Digital Business and Information System, Mahasarakham Business School, Mahasarakham University, Maha Sarakham Province, Thailand

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 * Corresponding Author. 

   Corresponding author's ORCID profile:  https://orcid.org/0000-0003-2448-4575

 Digital Object Identifier (DOI)

 
https://doi.org/10.21833/ijaas.2026.09.013

 Abstract

Military enlistment decisions are shaped by complex, multidimensional factors, including socioeconomic status and personal attitudes. This study develops a predictive decision model to identify the key factors that distinguish voluntary enlistment from conscription by lottery in Thailand's 23rd Military District. A dataset of 200 military personnel (147 volunteers and 53 drafted personnel) was analyzed using data mining techniques in Weka. The Random Tree and Random Forest algorithms were employed alongside three baseline classifiers: logistic regression, Naive Bayes, and J48. The models were evaluated based on accuracy, precision, recall, F1 score, and kappa statistics. Random Forest achieved the best balance between overall accuracy and sensitivity to the minority class (Accuracy = 92.86%, Kappa = 0.814, Weighted F1 = 0.926, Lottery-class F1 = 0.861; 10-fold cross-validation accuracy = 95.95% ± 0.44), outperforming all baseline models, particularly in classifying the minority (lottery/drafted) class, for which logistic regression achieved an F1 score of only 0.706. The most influential demographic predictors were occupation, province, and income. When attitudinal variables were included, cultural factors became the most important factor, followed by risk and legal perceptions. However, predictive accuracy did not improve compared with using demographic variables alone. These findings show that machine learning can effectively model enlistment behavior. The Royal Thai Army should develop recruitment policies that target specific occupations and geographical areas while strengthening cultural messaging and risk communication to increase voluntary enlistment rates.

 © 2026 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords

Military enlistment, Voluntary recruitment, Decision tree models, Random forest, Machine learning

 Article history

Received 18 May 2026, Received in revised form 28 August 2026, Accepted 23 September 2026

 Acknowledgment

This research project was financially supported by Mahasarakham Business School, Mahasarakham University, Thailand. The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation. 

 Compliance with ethical standards

 Ethical considerations

This study was reviewed and approved by the Mahasarakham University Ethics Committee for Research Involving Human Subjects, Thailand (Certificate of Approval No. 110-066/2025). Participation was entirely voluntary; all participants were informed of the objectives, procedures, and intended use of the data before data collection, and informed consent was obtained. No personally identifiable information was collected, and all responses were anonymized, treated as strictly confidential, and reported only in aggregate form for academic purposes. 

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

Phanngoen T, Chanthinok K, and Jantarajaturapath P (2026). Applying decision tree models to identify key factors influencing voluntary military enlistment: A case study of Thailand's 23rd Military District. International Journal of Advanced and Applied Sciences, 13(9): 130-142

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