• Kapudasi D M Mahima
• G Ravindra Bharathi

Lumpy skin disease (LSD) is a highly contagious viral infection that primarily affects cattle, leading to severe economic losses in the livestock industry. Climate factors significantly influence the occurrence and transmission dynamics of LSD, making it crucial to study their impact on disease outbreaks. This research explores the role of various climatic parameters in the diagnosis of LSD within a specific geographic region. In recent years, there has been increased interest in leveraging machine learning (ML) models and spatial data to forecast and manage LSD outbreaks. This study proposes an ensemble ML model, specifically gradient boosting (GB), for the classification of LSD. The GB model is evaluated against several standalone ML algorithms, including random forest (RF), supportvector machine (SVM), k-nearest neighbours (KNN), decision tree (DT), logistic regression (LR), and extreme gradient boosting (XGB). The performance of the GB model is shown to surpass the other models, achieving an accuracy of 98.97%and anROC-AUCof0.99. The results are compared with those from recent studies, and the proposed model demonstrates superior predictive capability, highlighting its potential for improving the accuracy of disease prediction and outbreak management in livestock.

K-nearest neighbors,decision tree,lumpyskin,machinelearning

"Advance Machine Learning Algorithms In Prediction Of Lumpy Skin Disease, A Contagious Capripox Virus Infection In Cattle", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 11, page no.c579-c586, November-2024, Available :http://www.jetir.org/papers/JETIR2411276.pdf

"Advance Machine Learning Algorithms In Prediction Of Lumpy Skin Disease, A Contagious Capripox Virus Infection In Cattle", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 11, page no. ppc579-c586, November-2024, Available at : http://www.jetir.org/papers/JETIR2411276.pdf