ABSTRACT
OBJECTIVES: 4.2 million people die every year from many diseases due to air pollution. The WHO confirms that 92% of the world's population lives in areas where the air quality limit is exceeded. In 251 days of 2011, the concentration of fine particulate matter in Ulaanbaatar exceeded the permissible level by 62%-76%. According to the results of the research, the content of fine particles decreased by 37%-46% in 2019. Because it is harmful to the health of children, we aimed to show the effect of air pollution on the mortality through data mining. METHODS: In many countries, research is being conducted to generate effective knowledge from big data using data mining methods. So, we are working to introduce this method to the health sector of Mongolia. In this study, we used the decision tree algorithms. RESULTS: We collected data on air pollution and under five mortality for 2019-2022 in Ulaanbaatar and created the database, built the models using the algorithms, and compared the results with the Mongolian standard. If the average of PM10 in winter is higher than the concentration specified in the standard, the mortality rate is likely to be high. Mortality is likely to be high if the nitrogen dioxide tolerance is high in the spring. CONCLUSION: The accuracy of the models calculated by the C5.0 algorithm is higher than the determined by the CART algorithm, the sensitivity and specificity values are higher than 0.50, so the mortality rates are uniformly predicted and low mortality prevails.
