Authors : Alirat Ibrahim; Umar Iliyasu; Rosemary Mrumun Dima

Volume/Issue : Volume 11 - 2026, Issue 10 - October

---

Google Scholar : https://tinyurl.com/2tybe37s

DOI : https://doi.org/10.38124/ijisrt/26Oct997

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

---

Abstract : Late detection of disease is a persistent problem for cattle keepers in Nigeria, where veterinary visits are few and most health checks still rely on what a farmer notices. This paper describes a prototype health-monitoring system that places each animal in one of four risk tiers (low, medium, high or critical) using 15 indicators that a farm worker can record without laboratory tests and compares six classifiers on the same task.

Keywords : Livestock Health Monitoring; Machine Learning; Risk Stratification; Data Leakage; Gradient Boosting; Cattle.

References :

1. AU-IBAR. (2024). Livestock productivity report: Africa regional overview. African Union Interafrican Bureau for Animal Resources.
2. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
3. Bushby, E. V., Thomas, M., Vázquez-Diosdado, J. A., Occhiuto, F., & Kaler, J. (2024). Early detection of bovine respiratory disease in pre-weaned dairy calves using sensor based feeding, movement, and social behavioural data. Scientific Reports. https://doi.org/10.1038/s41598-024-58206-4
4. Cantor, M. C., Casella, E., Silvestri, S., Renaud, D. L., & Costa, J. H. C. (2022). Using machine learning and behavioral patterns observed by automated feeders and accelerometers for the early indication of clinical bovine respiratory disease status in preweaned dairy calves. Frontiers in Animal Science, 3, Article 852359. https://doi.org/10.3389/fanim.2022.852359
5. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297. https://doi.org/10.1007/BF00994018
6. El Moutaouakil, K., & Falih, N. (2023). A design of a smart farm system for cattle monitoring. Indonesian Journal of Electrical Engineering and Computer Science, 32(2), 857–864. https://doi.org/10.11591/IJEECS.V32.I2.PP857-864
7. Ezanno, P., Picault, S., Beaunée, G., Bailly, X., Muñoz, F., Duboz, R., Monod, H., & Guégan, J.-F. (2021). Research perspectives on animal health in the era of artificial intelligence. Veterinary Research, 52, 40. https://doi.org/10.1186/s13567-021-00902-4
8. Faris, D. N., Gad, A. M., El-Tarabany, M. S., Ramadan, S. I., Afifi, G. G., & Manaa, E. A. (2024). Machine learning algorithms for clinical mastitis prediction in a dairy herd use automated milking system data. Journal of Advanced Veterinary Research, 14(6), 975–981.
9. Federal Ministry of Livestock Development. (2024). Nigeria livestock sector report 2023/2024. Government of Nigeria.
10. Food and Agriculture Organization. (2024). Livestock and disease in Africa: Economic and food security dimensions. FAO Publications.
11. Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29(5), 1189–1232. https://doi.org/10.1214/aos/1013203451
12. Garcia, R., Aguilar, J., Toro, M., Pinto, A., & Rodriguez, P. (2020). A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture, 179, 105826.
13. Girdauskaitė, A., Grigė, S., Džermeikaitė, K., Krištolaitytė, J., Malašauskienė, D., Televičius, M., Šertvytytė, G., Lembovičiūtė, G., & Antanaitis, R. (2025). Supervised machine learning approaches for early detection of metabolic and udder health disorders in dairy cows using sensor-derived data. Frontiers in Veterinary Science, 12, Article 1726719. https://doi.org/10.3389/fvets.2025.1726719
14. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
15. Grinsztajn, L., Oyallon, E., & Varoquaux, G. (2022). Why do tree-based models still outperform deep learning on typical tabular data? Advances in Neural Information Processing Systems, 35. https://arxiv.org/abs/2207.08815
16. Heinen, L., Larson, R. L., & White, B. J. (2025). Disease prediction in cattle: A mixed-methods review of predictive modeling studies. Animals, 15(17), Article 2481. https://doi.org/10.3390/ani15172481
17. Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression (3rd ed.). John Wiley & Sons.
18. Kapoor, S., & Narayanan, A. (2023). Leakage and the reproducibility crisis in machine-learning-based science. Patterns, 4(9), Article 100804. https://doi.org/10.1016/j.patter.2023.100804
19. Kim, S.-J., Jin, X.-C., Bharanidharan, R., & Kim, N.-Y. (2024). Monitoring multiple behaviors in beef calves raised in cow-calf contact systems using a machine learning approach. Animals, 14(22), Article 3278. https://doi.org/10.3390/ani14223278
20. Lasser, J., Matzhold, C., Egger-Danner, C., Fuerst-Waltl, B., Steininger, F., Wittek, T., & Klimek, P. (2021). Integrating diverse data sources to predict disease risk in dairy cattle: A machine learning approach. Journal of Animal Science, 99(12), skab294. https://doi.org/10.1093/jas/skab294
21. Megahed, A. A., Bommineni, Y. R., Short, M., Galvão, K. N., & Bittar, J. H. J. (2025). Using supervised machine learning algorithms to predict bovine leukemia virus seropositivity in Florida beef cattle: A 10-year retrospective study. Journal of Veterinary Internal Medicine, 39(3), Article e70070. https://doi.org/10.1111/jvim.70070
22. National Bureau of Statistics. (2023). Agricultural sector summary report 2022/2023. NBS Nigeria.
23. Panigrahy, B., Subudhi, A., Harichandan, T., Padhy, N., & Panigrahi, R. (2026). Comparative analysis of machine-learning and deep-learning approaches for accurate animal disease prediction and health risk assessment. Engineering Proceedings, 124(1), Article 52. https://doi.org/10.3390/engproc2026124052
24. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
25. Post, C., Rietz, C., Büscher, W., & Müller, U. (2020). Using sensor data to detect lameness and mastitis treatment events in dairy cows: A comparison of classification models. Sensors, 20(14), Article 3863. https://doi.org/10.3390/s20143863
26. Reddy, S. S., D R, M., S, J., & C, N. (2024). A comprehensive review of machine learning approaches in livestock health monitoring. Journal of Big Data Technology and Business Analytics, 3(3), 12–19. https://doi.org/10.46610/JBDTBA.2024.v03i03.002
27. Rojas, H. A., White, B. J., Amrine, D. E., & Larson, R. L. (2022). Predicting bovine respiratory disease risk in feedlot cattle in the first 45 days post arrival. Pathogens, 11(4), Article 442. https://doi.org/10.3390/pathogens11040442
28. Surana, J., & Sharma, S. K. (2024). Predicting cow health with a smart framework: A big data and deep learning-based IoT approach. International Journal of Intelligent Systems and Applications in Engineering, 12(4), 3201–3210.
29. Taneja, M., Byabazaire, J., Jalodia, N., Davy, A., Olariu, C., & Malone, P. (2020). Machine learning based fog computing assisted data-driven approach for early lameness detection in dairy cattle. Computers and Electronics in Agriculture, 171, Article 105286. https://doi.org/10.1016/j.compag.2020.105286
30. Tian, H., Zhou, X., Wang, H., Xu, C., Zhao, Z., Xu, W., & Deng, Z. (2024). The prediction of clinical mastitis in dairy cows based on milk yield, rumination time, and milk electrical conductivity using machine learning algorithms. Animals, 14(3), Article 427. https://doi.org/10.3390/ani14030427