Development of a Model for the Prediction of Lumpy Skin Diseases using Machine Learning Techniques
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Published:
Oct 20, 2023
Keywords:
Decision tree, K-nearest neighbor, Lumpy skin diseases, Random forest, Support vector machine
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Abstract
Lumpy skin diseases virus (LSDV) is a dangerous and contagious diseases that are mostly common in Sub-Saharan African, South Eastern Europe, South Asia and as well as Middle East, China. LSDV is transmitted through blood sucking insects which are double stranded DNA virus and belong to the family of Capri poxvirus genus family. The recent study proved and clarified that lumpy skin diseases viruses (LSDV) affected mostly cattle and buffalo in Africa, Asia and Europe with population of 29 966, 8 837 and 2 471 outbreaks respectively, between the years 2005 – 2021. Different machine learning approaches have been adopted for the prediction of lumpy skin diseases. An enhanced model was developed to improve the predictive performance of existing model and also, compared the performance of stacked ensemble of single classifiers with respect to optimized artificial neural network. The implementation was done with python 3.7 on Core i5, 16G RAM Intel hardware. The single classifiers are decision tree (DT), k-nearest neighbor, random forest (RF) and support vector machine (SVM). A feature wiz feature selection technique was adopted on lumpy skin diseases dataset coupled with the parameters tuning of the model before classification. Both stacked ensemble and optimized artificial neural network model outperformed the existing model. Stacked ensemble model gives accuracy, precision, f1-score and recall of 97.69%, 98.44%, 98.93% and 98.68% respectively. The results also showed that optimized artificial neural networks of 200 epochs outperformed stacked ensemble classifiers with accuracy of 98.89% and 98.66% of training and validation respectively. The developed model in a real world would assist in reducing the occurrence of lumpy skin diseases.
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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. https://doi.org/10.53982/ajerd.2023.0602.10-j
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[2] Mujumdar, A. & Vaidehi, V. Diabetes Prediction using Machine Learning Algorithms. Procedia Comput. Sci., 5(16), 292–299, 2019, doi: 10.1016/j.procs.2020.01.047.
[3] Adetunji, O. J., Adeyanju, I. A. & Esan, A. O. (2023). Flood Areas Prediction in Nigeria using Artificial Neural Network. 2023 Int. Conf. Sci. Eng. Bus. Sustain. Dev. Goals, 1–6, doi: 10.1109/SEB-SDG57117.2023.10124629.
[4] Sobowale, A., Olaniyan, O. M., Adetan, O., Olabiyisi, S. & Omidiora, E. (2020). Development of Fuzzy Rules for Cdss Based Neonatal Monitoring System. FUW Trends Sci. Technol. J., 3(5), 895–900.
[5] Arora, A. (2020). Optimization of state-of-the-art fuzzy-metaheuristic ANFIS-based machine learning models for fl ood susceptibility prediction mapping in the Middle Ganga Plain , India. Sci. Total Environ., 7(50), 141-565, doi: 10.1016/j.scitotenv.2020.141565.
[6] Shen, Z., Wu, Q., Wang, Z., Chen, G. & Lin, G. (2021). Diabetic Retinopathy Prediction by Ensemble Learning Based on Biochemical and Physical Data. Sensors, 1–19.
[7] Alazzam, M. B., Alassery, F. & Almulihi, A. (2021). Identification of Diabetic Retinopathy through Machine Learning. Mob. Inf. Syst., doi: 10.1155/2021/1155116.
[8] Batta, M. (2018). Machine Learning Algorithms - A Review. Int. J. Sci. Res., 8(18), 381–386, doi: 10.21275/ART20203995.
[9] Madhavan, M. V., Pande, S., Umekar, P., Mahore, T. & Kalyankar, D. (2021). Comparative analysis of detection of email spam with the aid of machine learning approaches. IOP Conf. Ser. Mater. Sci. Eng., doi: 10.1088/1757-899X/1022/1/012113.
[10] Sethi, C. C. (2020). E-Mail Spam Detection using Machine Learning and Deep Learning. Int. J. Res. Appl. Sci. Eng. Technol., 6(8), 981–985, doi: 10.22214/ijraset.2020.6159.
[11] Yile, A. O., Hongqi, L., Liping, Z., Sikandar, A. & Zhongguo, Y. (2019). The linear random forest algorithm and its advantages in machine learning assisted logging regression modeling, J. Pet. Sci. Eng., 4(17), 776–789, doi: 10.1016/j.petrol.2018.11.067.
[12] Ali, A., Ahmed, M., Naeem, S., Anam, S. & Ahmed, M. M. (2023). An Unsupervised Machine Learning Algorithms: Comprehensive Review, Int. J. Comput. Digit. Syst., 2(20), 210–242, doi: 10.12785/ijcds/130172.
[13] Bakumenko, A. & Elragal, A. (2022). Detecting Anomalies in Financial Data Using Machine Learning Algorithms. Systems, 5(10), 32-48, doi: 10.3390/systems10050130.
[14] 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.
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[16] Zou, Q., Qu, K., Luo, Y., Yin, D., Ju, Y. & Tang, H. (2018). Predicting Diabetes Mellitus With Machine Learning Techniques. Front. Genet., 9(1), 1–10, doi: 10.3389/fgene.2018.00515.
[17] Julius, A. O., Ayokunle, A. O. & Ibrahim, F. O. (2021). Early Diabetic Risk Prediction using Machine Learning Classification Techniques,. Int. J. Innov. Sci. Res. Technol., 9(6), 502–507.
[18] 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.
[19] 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.
[20] 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.
[21] 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.
[22] Sprygin, A., Pestova, Y., Wallace, D. B., Tuppurainen, E. & Kononov, A. V. (2019). Transmission of lumpy skin disease virus: a short review. Virus Res., doi: 10.1016/j.virusres.2019.05.015.
[23] Tuppurainen, E. (2021). Review: Vaccines and vaccination against lumpy skin disease. Vaccines 10(9), 1–22, doi: 10.3390/vaccines9101136.
[24] Patel, J. R. & Heldens, J. G. (2022). Immunoprophylaxis against important virus diseases of horses, farm animals and birds. Vaccine, 12(27), 1797–1810, doi: 10.1016/j.vaccine.2008.12.063.
[25] 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.
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[27] 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
[28] 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: doi: 10.1109/ICOIACT55506.2022.9971807.
[29] Chakraborty, S. (2017). Image based skin disease detection using hybrid neural network coupled bag-of-features. 2017 IEEE 8th Annu. Ubiquitous Comput. Electron. Mob. Commun. Conf., doi: New York, NY, USA, 242-246, doi: 10.1109/UEMCON.2017.8249038.
[30] Rai, G., Naveen, A., Hussain, A., Kumar, A., Ansari, A. & Khanduja, N (2021). A Deep Learning Approach to Detect Lumpy Skin Disease in Cows. Comput. Networks, Big Data IoT, .369-377, doi: 10.1007/978-981-16-0965-7_30.
[31] 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.
[32] 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.
[33] Mohammed, R. R. & Islam, M. K. (2022). Skin Disease Screening System Based on Smartphone Captured Images Using Deep Learning. First Int. Conf. Intell. Comput. Eng., 267-272. 10.1109/ICCCAS55266.2022.9824580.
[34] Ehsanallah, A.S. (2021). Lumpy Skin disease dataset. Mendeley Data, V1, doi 10.17632/7pyhbzb2n9.1, 2021.
[35] Saha, P., Patikar, S. & Neogy, S. (2020). A correlation - Sequential forward selection based feature selection method for healthcare data analysis. 2020 IEEE Int. Conf. Comput. Power Commun. Technol. GUCON, 69–72, doi: 10.1109/GUCON48875.2020.9231205.
[36] Tam, V. (2020). A comparative study of centroid-based, neighborhood-based and statistical approaches for effective document categorization. Object Recognit. Support. by user Interact. Serv. Robot., 2(4): 235–238, doi: 10.1109/ICPR.2002.1047440.
[37] Witten, I. H., Frank, E. & Hall, M. A. (2011). Data Mining: Practical machine learning tools and techniques, 3rd ed. San Francisco, CA,USA: Morgan Kaufmann Publishers Inc.
[38] Colas, F. & Brazdil, P. (2006). Comparison of SVM and some older classification algorithms in text classification tasks. IFIP Int. Fed. Inf. Process., 2(17), 169–178, doi: 10.1007/978-0-387-34747-9_18.
[39] Korde, V. & Mahender, C. N. (2012) . Text Classification and Classifiers: A Survey. Int. J. Artif. Intell. Appl., 2(3): 85–99, doi: 10.5121/ijaia.2012.3208.
[40] Seema, M. & Mamta, R. (2012). Decision Tree: Data Mining Techniques. Int. J. Latest Trends Eng. Technol., 3(1), 150–155
[41] Singh, N., Kumar, R. (2015). Diabetic retinopathy: an update. Indian J. Ophthalmol. 56(3), 179.
[42] Yuan, X., Yuan, F., Yang, J., Peng, G. & Buckles, B. P. (2003). Gene Expression Classification : Decision Trees vs . SVMs. 92–96
[43] Vapnik, V. (2000). The Nature of Statistical Learning Theory. New York: Springer-Verlag
[44] Baharudin, B., Lee, L. H. & Khan, K. (2010). A Review of Machine Learning Algorithms for Text-Documents Classification. J. Adv. Inf. Technol., 1(1), 4–20, doi: 10.4304/jait.1.1.4-20.
[45] Tyralis, H. & Papacharalampous, G. (2019). A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources. Water
[46] Cutler, A., Cutler, D. R. & Stevens, J. R. (2012). Ensemble Machine Learning. Ensemble Mach. Learn., doi: 10.1007/978-1-4419-9326-7.
[47] Han, S., Kim, H. & Seop, Y. (2020). Double random forest. Mach. Learn., no. July, pp. 1569–1586
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