Contagious Bovine Pleuropneumonia Prediction Using Random Forest Technique
DOI:
https://doi.org/10.62054/ijdm/0104.18Keywords:
Contagious bovine pleuropneumonia, economy, livestock, random forestAbstract
Contagious Bovine Pleuropneumonia (CBPP) is a highly infectious disease that poses a significant threat to cattle populations, particularly in sub-Saharan Africa, where livestock is a critical economic and food security resource. Early detection and intervention are essential to mitigate its impact on cattle productivity and rural livelihoods. This study aims to develop a predictive model for CBPP using the Random Forest (RF) algorithm, a robust and scalable machine learning technique renowned for its accuracy and ability to handle complex datasets. The research utilized a dataset of 847 samples collected from veterinary clinics in Yola, Adamawa State, and online sources. Key symptoms of CBPP, such as respiratory distress, nasal discharge, fever, and pleuritis, served as input features for the model. Preprocessing steps, including outlier detection, handling missing values, and data normalization, were applied to ensure the dataset's quality and reliability. The RF algorithm was trained and validated to classify CBPP cases effectively, with performance metrics such as accuracy, precision, recall, and specificity used to evaluate its effectiveness. The experimental results demonstrated an impressive prediction accuracy of 95.65%. Results demonstrated the RF model's high accuracy and robustness in predicting CBPP, offering a practical tool for early diagnosis and decision-making. The study further highlights the potential of deploying the model in a mobile application, enabling cattle breeders to perform preliminary diagnoses and seek timely veterinary intervention. This approach aligns with global efforts to integrate technology into agricultural practices, enhancing livestock disease management and supporting sustainable agricultural development. The findings contribute to the growing body of knowledge on applying machine learning to livestock disease prediction, showcasing Random Forest's potential to transform disease management strategies in the livestock sector.
References
Alhaji, N. B., Ankeli, P. I., Ikpa, L. T., and Babalobi, O. O. (2020). Contagious Bovine Pleuropneumonia: Challenges and prospects regarding diagnosis and control strategies in Africa. Veterinary Medicine: Research and Reports, 11, 71–85. https://doi.org/10.2147/VMRR.S180025
Atıl, H., and Akıllı, A. (2016). Comparison of artificial neural network and K-means for clustering dairy cattle. International Journal of Sustainable Agricultural Management and Informatics, 2(1), 40-52.
Beef Cattle Research Council (n.d). Nutritional qualities of beef. Retrieved January 18, 2024 from https://www.beefresearch.ca/topics/nutritional-qualities-of-beef/
Golden, C., Rothrock, M., and Mishra, A. (2019). Comparison between random forest and gradient boosting machine methods for predicting Listeria spp. prevalence in the environment of pastured poultry farms. Food Research International. 122. 10.1016/j.foodres.2019.03.062.
Gota, M., Ricardo, V. V., Fabyano, F. S., and Samodha, C. F. (2018). Big Data analytics and precision animal agriculture symposium: Machine learning and data mining advance predictive big data analysis in precision animal agriculture. Proceedings
Khanikar, D., Phookan A., and Gogoi, A. (2024). Artificial Neural Networks- An Introduction and Application in Animal Breeding and Production: A Review . Agricultural Reviews. 45(3): 480-487. doi: 10.18805/ag.R-2421.
Noone, V. K., Sai, T. Y., Sandeep, K., Sangamesha, V., and Sumanth, R. (2021). Symptoms diagnosis using machine learning model random forest. International Journal for Research in Applied Science and Engineering Technology (IJRASET), 9(6), 45–98. https://doi.org/10.22214/ijraset.v9i6.5783
Sana, R., Bhanushikha, R., and Roshan, L. (2023). Animal disease prediction using machine learning techniques. International Journal for Research in Applied Science and Engineering Technology (IJRASET), 11(6), 7–538.
Singh, R. K., and Sharma, V. C. (2015). Ensemble approach for zoonotic disease prediction using machine learning techniques.
Statista. (2023). Share of GDP by agricultural sector in Nigeria 2023. Retrieved December 23, 2023 from https://www.statista.com/statistics/1207940/share-of-gdp-by-agricultural- sector-in-nigeria/
Sumit, S. S., Awang Rambli, D. R., Mirjalili, S., Ejaz, M. M., and Restinet, M. S. U. (2022). On improving the performance of tiny-yolo-based CNN architecture for applications in human detection. Applied Sciences, 12(18), 9331.
Tengfei Z., Rhea, N., Wenting, Z., Xitian, P., Yu,S., Youxiang, Z., Qingping, L., Guoyuan, W., and Zhenyu, C.(2024). The impacts of animal agriculture on One Health—Bacterial zoonosis, antimicrobial resistance, and beyond, One Health,Volume 18,ISSN 2352- 7714,https://doi.org/10.1016/j.onehlt.2024.100748.
(https://www.sciencedirect.com/science/article/pii/S2352771424000740)
Uzonwanne, M. C., Francis, O. C., and Nwokoye, M. O. (2023). Impact of livestock production on gross domestic product in Nigeria. International Journal of Advanced Economics, 5(5).
Valdes-Donoso, P., VanderWaal, K., Jarvis, L. S., Wayne, S. R., and Perez, A. M. (2017). Using machine learning to predict swine movements within a regional program to improve control of infectious diseases in the US. Frontiers in Veterinary Science, 4(2). https://doi.org/10.3389/fvets.2017.00002
World Bank. (2021). GDP growth (annual %) Nigeria. Retrieved 22nd, December,2023https://data.worldbank.org/indicator.
Yoade, J. A., Ibrahim, H. I., and Ibrahim, H. Y. (2009). Analysis of women involvement in livestock production in Lafia area of Nasarawa State, Nigeria. Livestock Research for Rural Development, 21, Article #220. Retrieved December 18, 2023, from http://www.lrrd.org/lrrd21/12/ayoa21220.htm
Zhang, S., Su, Q., and Chen, Q. (2021). Application of machine learning in animal disease analysis and prediction. Current Bioinformatics, 16(7), 972–982.
Zhang, S., Su, Q., and Chen, Q. (2020). Application of machine learning in animal disease analysis and prediction. Current Bioinformatics, 15(7), 561–573. https://doi.org/10.2174/1574893615999200728195613
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