LEVERAGING BIG DATA FOR DOG HEALTH ANALYSIS: AN EXPLORATORY STUDY USING "TANBA" IN KAZAKHSTAN

In the era of artificial intelligence, collecting and analyzing data about dog health through electronic medical cards and passports has become a key factor in improving the quality of life for pets. In this study there was analyzed 93,922 records about dogs contained in Kazakhstan's pet registration information system “Tanba”. The research focused on the demographic characteristics of dogs, including breed, age, and region of residence. Explanatory Data Analysis was conducted using descriptive statistics, and Natural Language Processing (NLP) methods were applied to standardize breed names, improving data consistency. Additionally, an ANOVA test was performed to assess the impact of factors such as gender, region, breed, and breed size on dogs' lifespan. Based on the data analysis, there are highlights of key aspects such as the predominance of young dogs (average age 5.52 years), the high proportion of dogs without breed, and the high concentration of stray animals in some regions, which emphasizes the need for increased efforts to control the population and improve living conditions for stray dogs. This study presents an analysis of the dog population for 2024 based on data from the Tanba national registration system. Unlike previous studies that focused on the prevalence of individual diseases or were limited to data from specific regions, this study covers the entire country and provides a general overview of the dog population. The findings indicate a high proportion of mixed-breed and stray dogs in Kazakhstan, as well as significant regional differences in canine lifespan. Breed and regional factors have a statistically significant impact on lifespan, emphasizing the importance of considering these characteristics when developing programs to improve animal welfare and veterinary care. In the future, it is planned to improve data processing algorithms and expand the use of additional sources of information, which will allow for more accurate assessment of dog health risks and development of more effective preventive measures.