A Novel Ensemble Learning Technique for Lumpy Skin Disease Classification
Keywords:
ensemble method, gradient boosting, lumpy skin, machine learningAbstract
Lumpy skin disease is a highly contagious viral disease that affects cattle and has significant economic implications for the livestock industry. It has a direct relationship with climate, as the latter plays a major role in studying the infection and the pattern of transmission followed by it. This study illustrates how different climate parameters contribute to the diagnosis of LSD in cattle in a certain nation or location. In recent years, there has been a growing interest in using machine learning algorithms and geographical data to predict and mitigate the spread of LSD. Several studies have been conducted to explore the potential of machine learning (ML) models for predicting LSD outbreaks based on geographical data. In this paper, we built an effective ensemble ML model called gradient boosting (GB) to classify LSD. Our model outperforms other standalone ML models like random forest (RF), support vector machine (SVM), k-nearest neighbors (KNN), decision tree (DT), logistic regression (LR), and extreme GB (XGB). Further, we have compared our results against the recent past study results, and our model outperformed with accuracy (98.97) and ROC-AUC (0.99). By using these techniques, one can improve one's capacity to predict different diseases and natural occurrences.
Downloads
References
Afshari, S. & Ehsanallah. (2022). Assessing machine learning techniques in forecasting lumpy skin disease occurrence based on meteorological and geospatial features. Trop. Anim. Health Prod., 1(54), 12- 37, doi: 10.1007/s11250-022-03073-2.
Anwar, A., Na-Lampang, K., Preyavichyapugdee, N. & Punyapornwithaya, V. (2022). Lumpy Skin Disease Outbreaks in Africa, Europe, and Asia (2005–2022): Multiple Change Point Analysis and Time Series Forecast. Viruses, 10(14), 12-32, doi: 10.3390/v14102203.
Sarker, I. H. (2021). Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Comput.Sci., 3(2), 1–21, 2021, doi: 10.1007/s42979-021-00592-x.
Molla, W., de Jong, M .C., Gari, G. Frankena, K. (2017). Economic impact of lumpy skin disease and cost effectiveness of vaccination for the control of outbreaks in Ethiopia. Prev. Vet. Med., 7(14), 100–107, doi: 10.1016/j.prevetmed.2017.09.003.
Reddy, P., Viswanath, P. & Reddy, E. B. (2018). Semi-supervised learning: a brief review. Int. J. Eng. Technol., 18(7), 1- 18, doi: 10.14419/ijet.v7i1.8.9977.
Singh, V., Chen, S. S., Singhania, B. Nanavati, A. kumarkar, & Gupta, A. (2022). How are reinforcement learning and deep learning algorithms used for big data based decision making in financial industries–A review and research agenda. Int. J. Inf. Manag. Data Insights, 2(2), 100 - 109, doi: 10.1016/j.jjimei.2022.100094.
Saeed, S., Shaikh, A., M. Memon, M. A.& Naqvi, S. M. Impact of Data Mining Techniques to Analyze Health Care Data. J. Med. Imaging Heal. Informatics, 4(8), 682–690, doi: 10.1166/jmihi.2018.2385.
Afshari, S. & Ehsanallah. (2022). Assessing machine learning techniques in forecasting lumpy skin disease occurrence based on meteorological and geospatial features. Trop. Anim. Health Prod., 1(54), 12- 37, doi: 10.1007/s11250-022-03073-2.
Anwar, A., Na-Lampang, K., Preyavichyapugdee, N. & Punyapornwithaya, V. (2022). Lumpy Skin Disease Outbreaks in Africa, Europe, and Asia (2005–2022): Multiple Change Point Analysis and Time Series Forecast. Viruses, 10(14), 12-32, doi: 10.3390/v14102203.
Ujjwal, N.,Singh, A., Jain, A. K. & Tiwari, R. G. (2022). Exploiting Machine Learning for Lumpy Skin Disease Occurrence Detection. 2022 10th Int. Conf. Reliab. Infocom Technol. Optim. (Trends Futur. Dir. (ICRITO), Noida, India, 1–6, doi: doi: 10.1109/ICRITO56286.2022.9964656.
Dofadar, D. F., Abdullah, H.,. Khan, R. H., Rahman, R. & Ahmed, M. S. (2022). A Comparative Analysis of Lumpy Skin Disease Prediction Through Machine Learning Approaches. 2022 IEEE Int. Conf. Artif. Intell. Eng. Technol., doi: 10.1109/IICAIET55139.2022.9936742.
Pal, O. K. (2021). Skin Disease Classification: A Comparative Analysis of K-Nearest Neighbors (KNN) and Random Forest Algorithm. Int. Conf. Electron. Commun. Inf. Technol. (ICECIT), Khulna, Bangladesh, 1-5, doi 10.1109/ICECIT54077.2021.9641120
Suparyati, E. U. & Muhammad, A. H. (2022). Lumpy Skin Disease Prediction Based on Meteorological and Geospatial Features using Random Forest Algorithm with Hyperparameter Tuning.. 5th Int. Conf. Inf. Commun. Technol., p. Yogyakarta, Indonesia, 99–104, doi: 10.1109/ICOIACT55506.2022.9971807.
Mohammed, S. S. & Al-Tuwaijari, J. M. (2021). Skin Disease Classification System Based on Machine Learning Technique: A Survey. IOP Conf. Ser. Mater. Sci. Eng., doi: 10.1088/1757-899x/1076/1/012045.
Lake, B., Getahun, F. & Teshome, F. (2022). Application of Artificial Intelligence Algorithm in Image Processing for Cattle Disease Diagnosis. J. Intell. Learn. Syst. Appl., 71–88, doi: 10.4236/jilsa.2022.144006.
Punyapornwithaya V. (2023). Forecasting of daily new lumpy skin disease cases in Thailand at different stages of the epidemic using fuzzy logic time series, NNAR, and ARIMA methods. Preventive Veterinary Medicine, https://doi.org/10.1016/j.prevetmed.2023.105964.
Kshirsagar, P. R., Manoharan, H., Shitharth, S., Alshareef, A. M., Albishry, N. & Balachandran, P. K. (2022). Deep Learning Approaches for Prognosis of Automated Skin Disease. Life 2022, 12, 426, https://doi.org/10.3390/life12030426.
A. Kumar, B. Kumar and H. S. Negi, "Predicting Lumpy Skin Disease using Various Machine Learning Models," 2023 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES), Greater Noida, India, 2023, pp. 412-416, doi: 10.1109/CISES58720.2023.10183604.
A. K. Jain, R. G. Tiwari, N. Ujjwal and A. Singh, "An Explainable Machine Learning Model For Lumpy Skin Disease Occurrence Detection," 2022 International Conference on Data Analytics for Business and Industry (ICDABI), Sakhir, Bahrain, 2022, pp. 503-508, doi: 10.1109/ICDABI56818.2022.10041665.
lumpy skin disease - Google Search. (n.d.). accessed on 28 Mar, 2024 https://www.google.com/search?sca_esv=65d2b102cd6037ef&sxsrf=ACQVn09vsuDxqGyO37WWdieHxztnX4iFyg:1709540060046&q=lumpy+skin+disease&tbm=isch&source=lnms&sa=X&ved=2ahUKEwjmkIiAldqEAxV7y6ACHcXBBCAQ0pQJegQIEhAB&biw=1366&bih=625&dpr=1
Afshari Safavi, Ehsanallah (2021), “Lumpy Skin disease dataset”, Mendeley Data, V1, doi: 10.17632/7pyhbzb2n9.1
Yasi, Dani., Agus, Gunawan., Sapto, Wahyu, Indratno. (2022). Detecting Online Outlier for Data Streams using Recursive Residual. doi: 10.1109/ICIC56845.2022.10006916
Magar, V., Ruikar, D., Bhoite, S., & Mente, R. (2024). Innovative Inter Quartile Range-based Outlier Detection and Removal Technique for Teaching Staff Performance Feedback Analysis. Journal of Engineering Education Transformations, 37(3).
Kavitha, K., Chittineni, S., Andhavarpu, N. K., & Chappa, V. (2022, January). Textual Sentimental Classification using Convolution Neural Network Algorithm. In 2022 International Conference on Computing, Communication and Power Technology (IC3P) (pp. 246-249). IEEE.
Vital, T. P. (2023). Intellectual Gestational Diabetes Diagnosis System Using MLP-Whale Optimization Algorithm including Statistical Analysis. International Journal of Computing and Digital Systems, 14(1), 1-1.
JagadeeswaraRao, G., Sivaprasad, A. An integrated ensemble learning technique for gene expression classification and biomarker identification from RNA-seq data for pancreatic cancer prognosis. Int. j. inf. tecnol. 16, 1505–1516 (2024). https://doi.org/10.1007/s41870-023-01688-8
Rao GJ, Prasad AS. Classification of gene expression from RNA-seq data for pancreatic cancer prognosis using ensemble learning. J App Biol Biotech. 2024;12(3):45-53. DOI: 10.7324/JABB.2024.171755
Matthews, B. W. (1975). "Comparison of the predicted and observed secondary structure of T4 phage lysozyme". Biochimica et Biophysica Acta (BBA) - Protein Structure. 405 (2): 442–451. doi:10.1016/0005-2795(75)90109-9. PMID 1180967.
Olaniyan, O. M., Adetunji, O. J., & Fasanya, A. M. (2023). Development of a model for the prediction of lumpy skin diseases using machine learning techniques. ABUAD Journal of Engineering Research and Development, 6(2), 100-112.
Afshari, S. & Ehsanallah. (2022). Assessing machine learning techniques in forecasting lumpy skin disease occurrence based on meteorological and geospatial features. Trop. Anim. Health Prod., 1(54), 12- 37, doi: 10.1007/s11250-022-03073-2.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
