Abstract

In many countries, research is being conducted to generate effective knowledge from big data using data mining methods. These methods have been tested on data such as air pollution, diabetes, cardiovascular, adolescent emotions, etc., creating valuable knowledge and contributing to the field of health sciences in Mongolia. We tested the decision tree algorithms on the data of children under five years of age and PM10 and PM2.5 fine particles for each month of 2019-2020 [PS1] [J2] , and the C50 method was highly effective in building and evaluating classification tree models.

Globally, one in seven people between the ages of 10 and 19 have a mental disorder, which is 13% of adolescents. The main causes of illness and disability in adolescents is depression, anxiety and behavioural disorders [1]. On the report Mental Health System's in Mongolia of World Health Organization, for Mongolia, special attention needs to be given to develop professional competence and services in the area adolescent mental health and considered the need to expand mental health research and publish articles in indexed journals [PS3] [J4]  [2]. Considered the need to expand mental health research and publish scientific articles in indexed journals.

Consequently, the SDQ were taken from students, class teachers and parents of the 6-12th grade of Govi-Altai Province to evaluate the student’s emotions and created student, parent-guardian, and teacher evaluation databases.

When divide the student evaluation database into ten using cross-validation and create the models by C50, Bayes, Ripper methods, evaluate by measures such as sensitivity, specificity, accuracy, Bayes method model showed good result.

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