Aplicação de Machine Learning no Diagnóstico da Peste des Petits Ruminants: Uma Abordagem Comparativa entre Classificação e Clusterização
Resumo
A Peste dos Pequenos Ruminantes (PPR) é uma doença infecciosa que afeta caprinos e ovinos, causando impactos econômicos e sanitários significativos. Métodos tradicionais de diagnóstico, como o RT-qPCR, embora precisos, demandam tempo e infraestrutura. Este estudo avalia o uso de técnicas de Machine Learning para auxiliar no diagnóstico da PPR a partir de dados clínicos. Foram aplicados modelos de classificação e clusterização para identificar padrões associados à presença da doença. O Gradient Boosting obteve os melhores resultados preditivos, enquanto a análise por clusterização indicou estruturas relevantes nos dados. Os achados apontam para o potencial dessas abordagens no apoio à detecção e monitoramento da PPR.Referências
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Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society: Series B (Methodological), 20(2):215–232.
Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of statistics, pages 1189–1232.
Hubert, L. and Arabie, P. (1985). Comparing partitions. Journal of classification, 2(1):193–218.
Kodinariya, T. M. and Makwana, P. R. (2013). Review on determining number of cluster in k-means clustering. International Journal, 1(6):90–95.
Kohavi, R. et al. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In Ijcai, volume 14, pages 1137–1145. Montreal, Canada.
Kohli, V., Arora, A., and Dagar, P. (2017). A study on disease prediction using machine learning in healthcare. International Journal of Information Technology, 9:119–124.
MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. 1(14):281–297.
McCallum, A. and Nigam, K. (1998). A comparison of event models for naive bayes text classification. In AAAI-98 workshop on learning for text categorization, volume 752, pages 41–48.
Myagila, K. C., Sabas, J., and Mark, P. N. (2023). Multi-model ppr disease prediction using machine learning algorithms. In 2023 First International Conference on the Advancements of Artificial Intelligence in African Context (AAIAC), pages 1–6. IEEE.
Nguyen, D. C., Ding, M., Pathirana, P. N., and Seneviratne, A. (2020). Artificial intelligence in the battle against coronavirus (covid-19): a survey and future research directions. arXiv preprint arXiv:2008.07343.
Niu, B., Liang, R., Zhou, G., Zhang, Q., Su, Q., Qu, X., and Zhang, S. (2021). Prediction for global peste des petits ruminants outbreaks based on a combination of random forest algorithms and meteorological data. Frontiers in Veterinary Science, 7:570829.
Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics, 20:53–65.
Satopaa, V., Albrecht, J., Irwin, D., and Raghavan, B. (2011). Finding a ”kneedle”in a haystack: Detecting knee points in system behavior. pages 166–171.
Sobeih, M. M., Rahman, A. K. M. A., Islam, S. S., Sufian, M. A., Talukder, M. H., Ward, M. P., and Martínez-López, B. (2020). Peste des petits ruminants risk factors and space-time clusters in bangladesh. Frontiers in Veterinary Science, 7:572432.
Thanyambo, D. (2023). Ppr disease data from goats and sheep. [link]. Acesso em: abr. 2025.
Thorndike, R. L. (1953). Who belongs to the family? Psychometrika, 18(4):267–276.
Walsh, M. G., Haseeb, M. A., and Mor, S. M. (2021). Prediction for global peste des petits ruminants outbreaks based on a combination of random forest algorithms and meteorological data. Frontiers in Veterinary Science, 7:570829.
Xu, D. and Tian, Y. (2015). A comprehensive review of clustering algorithms. Annals of Data Science, 2(2):165–193.
Xu, L., Skoularidou, M., Cuesta-Infante, A., and Veeramachaneni, K. (2019). Modeling tabular data using conditional gan. arXiv preprint arXiv:1907.00503.
Breiman, L. (2001). Random forests. Machine learning, 45(1):5–32.
Cortes, C. and Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3):273–297.
Cover, T. M. and Hart, P. E. (1967). Nearest neighbor pattern classification. IEEE transactions on information theory, 13(1):21–27.
Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society: Series B (Methodological), 20(2):215–232.
Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of statistics, pages 1189–1232.
Hubert, L. and Arabie, P. (1985). Comparing partitions. Journal of classification, 2(1):193–218.
Kodinariya, T. M. and Makwana, P. R. (2013). Review on determining number of cluster in k-means clustering. International Journal, 1(6):90–95.
Kohavi, R. et al. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In Ijcai, volume 14, pages 1137–1145. Montreal, Canada.
Kohli, V., Arora, A., and Dagar, P. (2017). A study on disease prediction using machine learning in healthcare. International Journal of Information Technology, 9:119–124.
MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. 1(14):281–297.
McCallum, A. and Nigam, K. (1998). A comparison of event models for naive bayes text classification. In AAAI-98 workshop on learning for text categorization, volume 752, pages 41–48.
Myagila, K. C., Sabas, J., and Mark, P. N. (2023). Multi-model ppr disease prediction using machine learning algorithms. In 2023 First International Conference on the Advancements of Artificial Intelligence in African Context (AAIAC), pages 1–6. IEEE.
Nguyen, D. C., Ding, M., Pathirana, P. N., and Seneviratne, A. (2020). Artificial intelligence in the battle against coronavirus (covid-19): a survey and future research directions. arXiv preprint arXiv:2008.07343.
Niu, B., Liang, R., Zhou, G., Zhang, Q., Su, Q., Qu, X., and Zhang, S. (2021). Prediction for global peste des petits ruminants outbreaks based on a combination of random forest algorithms and meteorological data. Frontiers in Veterinary Science, 7:570829.
Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics, 20:53–65.
Satopaa, V., Albrecht, J., Irwin, D., and Raghavan, B. (2011). Finding a ”kneedle”in a haystack: Detecting knee points in system behavior. pages 166–171.
Sobeih, M. M., Rahman, A. K. M. A., Islam, S. S., Sufian, M. A., Talukder, M. H., Ward, M. P., and Martínez-López, B. (2020). Peste des petits ruminants risk factors and space-time clusters in bangladesh. Frontiers in Veterinary Science, 7:572432.
Thanyambo, D. (2023). Ppr disease data from goats and sheep. [link]. Acesso em: abr. 2025.
Thorndike, R. L. (1953). Who belongs to the family? Psychometrika, 18(4):267–276.
Walsh, M. G., Haseeb, M. A., and Mor, S. M. (2021). Prediction for global peste des petits ruminants outbreaks based on a combination of random forest algorithms and meteorological data. Frontiers in Veterinary Science, 7:570829.
Xu, D. and Tian, Y. (2015). A comprehensive review of clustering algorithms. Annals of Data Science, 2(2):165–193.
Xu, L., Skoularidou, M., Cuesta-Infante, A., and Veeramachaneni, K. (2019). Modeling tabular data using conditional gan. arXiv preprint arXiv:1907.00503.
Publicado
28/05/2025
Como Citar
ARAÚJO, Rafael L.; SILVA, Vitor R. F. Da; SANTOS, Francisco E.; LUZ, Anthony I. M.; V. E SILVA, Romuere R..
Aplicação de Machine Learning no Diagnóstico da Peste des Petits Ruminants: Uma Abordagem Comparativa entre Classificação e Clusterização. In: ENCONTRO UNIFICADO DE COMPUTAÇÃO DO PIAUÍ (ENUCOMPI), 17. , 2025, Teresina/PI.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 149-158.
DOI: https://doi.org/10.5753/enucompi.2025.9782.
