Adoption Factor Prediction to Prevent Euthanasia Based on Artificial Intelligence

  • 투고 : 2021.02.15
  • 심사 : 2021.06.05
  • 발행 : 2021.06.30


In this paper, we analyzed the factors of adoption and implemented a predictive model to activate the adoption of animals. Recently, animal shelters are saturated due to the abandonment and loss of companion animals. To address this, we need to find a way to encourage adoption. In this paper, a study was conducted using two data from an open data portal provided by Austin, Texas. First, a correlation analysis was conducted to identify the attributes that affect the result value, and it was found that Animal Type Intake, Intake Type, and Age upon Outcome influence the Outcome Type with correlation coefficients of 0.4, 0.26, and -0.2, respectively. For these attributes, the analysis was conducted using Multiclass Logistic Regression. As a result, dogs had a higher probability of Adoption than cats, and animals subjected to euthanasia were more likely to adopt. In the case of Public Assist and Stray, it was found that the Missing rate was high. Also, the length of stay for cats increased to 12.5 years of age, while dogs generally adopted smoothly at all ages. These results showed an overall accuracy of 62.7% and an average accuracy of 91.7%, showing a fairly reliable result. Therefore, it seems that it can be used to develop a plan to promote the adoption of animals according to various factors. Also, it can be expanded to various services by interlocking with the webserver.



This work was supported by the research grant of the KODISA Scholarship Foundation in 2021.


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