Prediction analysis condition animal use algorithm (SVM+KNN)
DOI:
https://doi.org/10.35335/jict.v15i2.173Keywords:
Health Data Animal, K-Nearest Neighbors (KNN), Machine Learning Algorithms, Prediction Condition Animal, Support Vector Machine (SVM)Abstract
This research focuses on the development of a prediction model. Condition animals use Support Vector Machine (SVM) and K-nearest neighbors (KNN) algorithms. In animal husbandry and health animals, the ability to monitor and analyze condition health in real-time animal monitoring is essential to ensure welfare and productivity. Animals. The SVM and KNN algorithms were selected Because of their advantages in classification and regression tasks. The dataset used covers various health parameters for animals, such as temperature body, heart, diet, daily activity, and health data historical. This study shows that SVM and KNN algorithms are very accurate high in predicting the condition of healthy animals, with SVM achieving an accuracy of 97.63% and KNN achieving an accuracy of 97.16%. This prediction model allows detection early detection of health problems in animals so that the breeder and doctor animals can take action preventive and curative more quickly. The results of this study indicate that The combination of SVM and KNN can provide better predictions. Accurate and reliable, which ultimately will increase the health and well-being of animals.
References
Bai, X., Xu, S., & Yu, H. (2020). A hybrid SVM-KNN classifier for classification of 3D surface shapes. Pattern Recognition Letters, 139, 116–124. https://doi.org/10.1016/j.patrec.2020.10.001
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297. https://doi.org/10.1023/A:1022627411411
Chen, X., et al. (2017). Multimodal disease risk prediction using deep learning. Scientific Reports, 7(1), 1–13. https://doi.org/10.1038/s41598-017-05072-1
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning. Springer. https://doi.org/10.1007/978-0-387-84858-7
Kaur, M., & Sharma, M. (2019). Diagnosis of human psychological disorders using supervised learning and nature-inspired computing techniques. Journal of Supercomputing, 75, 8077–8102. https://doi.org/10.1007/s11227-019-02871-7
Leoni, A., et al. (2017). Evaluation of fuzzy and tree-based models in disease prediction. Artificial Intelligence in Medicine, 90, 45–56. https://doi.org/10.1016/j.artmed.2017.06.003
López, J. A., et al. (2020). Applications of machine learning in precision livestock farming. Frontiers in Veterinary Science, 7, 264. https://doi.org/10.3389/fvets.2020.00264
Mitchell, T. M. (1997). Machine Learning. McGraw Hill. https://doi.org/10.1007/978-0-387-84858-7
Noble, W. S. (2006). What is a support vector machine? Nature Biotechnology, 24(12), 1565–1567. https://doi.org/10.1038/nbt1206-1565
Pérez-Sánchez, J., et al. (2020). Machine learning for sustainable animal health. Scientific Reports, 10(1), 14330. https://doi.org/10.1038/s41598-020-71572-4
Pereira, R. J., et al. (2019). Livestock monitoring using sensor-based technologies: A systematic review. Computers and Electronics in Agriculture, 167, 105060. https://doi.org/10.1016/j.compag.2019.105060
Qiao, Y., et al. (2021). Enhancing livestock disease prediction with machine learning. PLoS ONE, 16(1), e0244941. https://doi.org/10.1371/journal.pone.0244941
Quinlan, J. R. (1996). Improved use of continuous attributes in C4.5. Journal of Artificial Intelligence Research, 4, 77–90. https://doi.org/10.1613/jair.279
Suresh, H., et al. (2018). Real-time animal health monitoring system using IoT and machine learning. Procedia Computer Science, 132, 865–872. https://doi.org/10.1016/j.procs.2018.05.130
Thieme, O., et al. (2020). Predictive analytics in veterinary science using data-driven models. Animals, 10(3), 395. https://doi.org/10.3390/ani10030395
Vapnik, V. N. (1999). The nature of statistical learning theory. Springer. https://doi.org/10.1007/978-1-4757-3264-1
Vijayarani, S., & Dhayanand, S. (2015). Liver disease prediction using SVM and Naive Bayes algorithms. Procedia Computer Science, 48, 471–475. https://doi.org/10.1016/j.procs.2015.04.126
Wang, J., et al. (2021). Comparative study of algorithms in precision livestock farming. Computers and Electronics in Agriculture, 175, 105607. https://doi.org/10.1016/j.compag.2020.105607
Zhao, X., et al. (2020). Genomic prediction with support vector machines in agriculture. Frontiers in Genetics, 11, 598318. https://doi.org/10.3389/fgene.2020.598318
Zupan, B., et al. (2001). Machine learning methods for disease risk prediction. Journal of Biomedical Informatics, 34(5), 329–340. https://doi.org/10.1016/S1532-0464(01)12014-3
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