Comparision of Machine Learning Models for Adolescent’s Emotional and Behavioral Problems
- VernacularTitle:Өсвөр үеийнхний сэтгэл хөдлөл, зан үйлийн машин сургалтын загваруудыг харьцуулсан дүн
- Author:
Batnast G
1
,
2
;
Akhyt T
1
,
2
;
Javzmaa T
1
,
2
;
Nyamdavaa U
1
,
2
;
Bayarmaa V
3
;
Purevdolgor L
1
,
2
;
Ajnai L
1
,
2
;
Enkh-Urel E
1
,
2
;
Galbadrakh Ch
1
,
2
;
Bat-Enkh O
4
;
Baatarkhuu Ts
4
Author Information
1. Department of Physics &
2. Medical Informatics, School of Biomedicine, MNUMS
3. National Center of Mental Health
4. Department of Information Technology, School of Information and Communication Technology
- Publication Type:Journal Article
- Keywords:
Data mining;
Accuracy;
specificity;
Sensitivity;
Measure;
Statistical tests
- From:
Mongolian Journal of Health Sciences
2025;86(2):116-120
- CountryMongolia
- Language:Mongolian
-
Abstract:
Background:Globally, an estimated 13% of adolescents aged 10–19 are affected by mental disorders. As of 2020, the
number of children under the age of 19 in Mongolia was reported to be 1,289,587, reflecting a 0.9% increase compared
to 2015. A 2013 study on adolescents found that 60.5% were categorized as mentally healthy, 30.5% exhibited emotional
and behavioral difficulties, and 9% were diagnosed with a mental disorder. A study conducted in Govi-Altai Province
between 2018 and 2019 reported that 58.6% of adolescents were classified as healthy, 36.1% experienced psychological
difficulties, and 5.2% were diagnosed with a mental disorder.
Internationally, adolescent mental health has been widely assessed using the Strengths and Difficulties Questionnaire
(SDQ), with an increasing number of studies employing artificial intelligence-based predictive models. However, in Mongolia,
research utilizing artificial intelligence and machine learning for analyzing large-scale mental health data remains
limited. This gap underscores the need for the present study.
Aim:Machine learning models were compared to determine adolescent emotional and behavioral problems using the
SDQ.
Materials and Methods:Data was collected from teenagers, teachers, and parents in Govi-Altai Province, and the databases
were created for each group. The teenager database was divided into 10 folds by cross-validation, and the models
were developed using machine learning methods and evaluated using their performance measures. The results were
mainly analyzed using the Bayes model.
Results:The teenagers have emotional and behavioral problems due to emotional and peer interactions, but they are at
risk of developing disorders due to hyperactivity and behavioral changes.
Conclusion:Comparing the model performance results with previous studies, Bayesian model accuracy decreased by
0.03, sensitivity decreased by 0.08, and specificity increased by 0.01. Also, the difference between the performance evaluation
metrics of the C50 and Bayesian models is very small, between 0.01 and 0.02. This shows that the performance of
the Bayesian method is good when the number of attributes in the database increases.
Compared to the results of the knowledge generated by the research, the participants are more likely to develop emotional
and behavioral disorders due to their peer relationship indicators, such as other children generally not liking them, getting
on better with adults, and due to emotional symptoms such as being unhappy and depressed.
- Full text:202505291243548738116-120.pdf
