Predicting Lumpy Skin Disease using Various Machine Learning Models

As infection with the virus of lumpy skin disease, which causes acute or subacute sickness in cattle and the population of water buffalo, poses a serious threat to the cattle industry. Machine learning models gives the direction to make ease of testing the lumpy disease. To predict Lumpy skin disease various machine learning models are available. Various parameters are considered to find out the numerous values such as accuracy, MAE, MSE and RMSE. In this paper distinguish machine learning models are considered to make prediction level high. This paper works for the different ML models on the lumpy disease prediction to forecast the cattle disease. Random forest model gives the highest accuracy which is 97.10% and poly-F is at second position which is 96.77
^%
accuracy score. Random forest gives MAE closet value to 0 which is 0.028 that indicates the best ML model with respect to error as well. The next ML model is decision tree, the value is 0.037. For MSE value, poly-F model gives remarkable output which is 0.025 and random forest with having 0.028 value. For RMSE, poly-F model gives remarkable output which is 0.16, random forest with having 0.017 value and decision tree is at 0.19.