DOI QR코드

DOI QR Code

Adoption Factor Prediction to Prevent Euthanasia Based on Artificial Intelligence

  • Received : 2021.02.15
  • Accepted : 2021.06.05
  • Published : 2021.06.30

Abstract

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.

Keywords

Acknowledgement

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

References

  1. Hong, J. Y. (2015). Mobile app design to promote adoption of abandoned animals. Domestic Master's Thesis Ewha Womans University Graduate School of Design, Seoul.
  2. Yu, H. S. (2019). A Study on the Adoption Prediction Model according to the Characteristics of Abandoned and Lost Dogs. Domestic Master's Thesis Graduate School, Dongguk University, Seoul.
  3. Protopopova, A., & Wynne, C. D. L. (2014). Adopter-dog interactions at the shelter: Behavioral and contextual predictors of adoption. Applied Animal Behaviour Science, 157, 109-116. https://doi.org/10.1016/j.applanim.2014.04.007
  4. Choi, S. E., Yu, H. S., Jung, H. W., Park, Y. M., & Lee, G. J. (2019). Prediction Model for Adoption Probability of Abandoned or Lost Dogs at Animal Shelters of Local Governments. Journal of the Korean Data Analysis Society, 21(5), 2365-2378. https://doi.org/10.37727/jkdas.2019.21.5.2365
  5. Animal People. (2020). "Pain Death" without anesthesia... Abandoned animal protection system collapses. Retrieved November 12, 2020 from http://www.hani.co.kr/arti/animalpeople/companion_animal/969697.html.
  6. Jang, E. H. (2013). Protection Systems of 'Living Trash', Abandoned Animals: A Case of Daegu City. Domestic Master's Thesis Graduate School, Kyungpook National University, Daegu
  7. Posage, J. M., Bartlett, P. C., Thomas, D.K. (1998). Determining factors for successful adoption of dogs from an animal shelter. J Am Vet Med Assoc, 213(4), 478-82.
  8. Merry, L. P., Kass, H., & Hart, L. A. (2002). Prediction of Adoption Versus Euthanasia Among Dogs and Cats in a California Animal Shelter, Journal of Applied Animal Welfare Science, 5(1), 29-42. https://doi.org/10.1207/S15327604JAWS0501_3
  9. Kang, M. S., Kang, H. J., Yoo, K. B., Ihm, C. H., & Choi, E. S. (2018). Getting started with Machine Learning using Azure Machine Learning studio. Seoul, Korea: Hanti media.
  10. Wikipedia. (2020). Retrieved August 16, 2020 from https://ko.wikipedia.org/wiki/%EC%A7%80%EB%8F%84_%ED%95%99%EC%8A%B5.
  11. Lee, M. W. (2020). Analysis of errors by language area of Korean learners using multinomial logistic regression analysis. Bilingualism, 79, 135-160.
  12. Nam, Y. J., & Shin, W. J. (2019). A Study on Comparison of Lung Cancer Prediction Using Ensemble Machine Learning, Korean Journal of Artificial Intelligence, 7(2), 19-24 https://doi.org/10.24225/kjai.2019.7.2.19
  13. Kwak, Y. S., & Kang, M. S. (2020). A Study on Methods to Prevent the Spread of COVID-19 Based on Machine Learning, Korean Journal of Artificial Intelligence, 8(1), 7-9. https://doi.org/10.24225/kjai.2020.8.1.7
  14. You, S. H., & Kang, M. S. (2020). A Study on Methods to Prevent Pima Indians Diabetes using SVM, Korean Journal of Artificial Intelligence, 8(2), 7-10. https://doi.org/10.24225/kjai.2020.8.1.7
  15. Lee, J. G., Cho, I. P., & Lee, S. Y. (2020). A Study on Data Collection Environment and Analysis Using Virtual Server Hosting of Azure Cloud Platform. Journal of the Korean Society of Computer Information, 28(2), 329-330.
  16. Seo, M. J., Yoo, H. J., Jang, & S. W. (2020). Azure cloud-based environment monitoring responsive web app development. Journal of the Korean Society of Mechanical Engineers, 1, 257-257.
  17. Cho, J. J., Jang, M. W., & Beak, S. H. (2020). Research on sign recognition techniques based on Azure IoT Edge. Journal of the Korean Institute of Communication Sciences, 12, 828-829.
  18. Yoo, K. Y. (2017). Introduction of the Seoul Companion Animal Center. Policy Report, (222), 1-24.
  19. Moon, Y. J., Lee, E. J., Jang, W. Y., Han, J. Y., & Hong, D. S. (2018). Virtual adoption service experience design for sponsoring abandoned animals. Proceedings of the Korean Society of Design Science 326-327.
  20. Park, H. M., & Park, S. Y. (2019). Issues and Alternatives to Companion Animal Policy. Issue & Diagnosis, 1, 1-25.
  21. Bae, J. E. & Kim, S. I. (2015). Development of digital contents for organic seedlings. Digital Design Studies, 15(1), 155-164. https://doi.org/10.17280/jdd.2015.15.1.015
  22. Choi, E. S., Yoo, H. J., Kang, M. S., & Kim, S. A. (2020). Applying Artificial Intelligence for Diagnostic Classification of Korean Autism Spectrum Disorder, ORIGINAL ARTICLE, 17(11), 1090-1095.
  23. Song, S. W., Kwak, Y. S., & Kang, M. S. (2020). A Study on Graph-based Weighted KNN based on Machine Learning, Test Engineering & Management, 83, 12264-12271.