ORGANIZATION OF THE SYSTEM OF REMOTE MONITORING OF STUDENTS' HEALTH

Abstract

Continuing education of young people after school can lead to the fact that they adapt to a new environment, which in most cases will lead to a change in the usual way of life and an increase in psychoemotional loads both for those who move to another city and for those who go to University, which can lead to various diseases. Since the future of the country is in the hands of young people continuing this education, it is better not to neglect it. The purpose of educational environments is to help young people adapt to a healthier lifestyle in the early stages of life. The urgency of such problems leads to the need for comprehensive studies that allow preclinically assess the current state of health of students and form a preventive environment in the educational environment and strengthen health. The work studied in this article was carried out on the basis of collecting and analyzing information about the health status of students of the Al-Farabi KazNU University. For this purpose, a simulation was carried out using personal health data of 3344 university students. 58.4% of girls and 41.6% of boys participated in the study. Most of the students who participated in the study were between the ages of 17 and 20. In this analysis, the k-nearest neighbors method, the reference vector method and the Bayesian algorithm were used. Sensitivity, accuracy and accessibility were taken into account with respect to selected performance indicators for evaluating various forecasting algorithms. It is established that when using the classifier of vectors of maximum and machine supports for these values, it shows high results. The overall performance of modern advanced intelligent information and analytical systems is estimated mainly by the reliability parameter. Compared with other well-known systems for monitoring the health of students, this system demonstrates high reliability. This model can be used to expand health monitoring by including external parameters that may affect the health of students. In addition, other educational institutions in Kazakhstan and around the world have plans to introduce deep learning technology to monitor the health of students.