Exploiting Machine Learning for Lumpy Skin Disease Occurrence Detection
Neha Ujjwal, Anshbir Singh, Anuj Kumar Jain, Raj Gaurang Tiwari
Abstract
Animals are very important to the human being. Humans are dependent on most things like milk, curd, honey, etc. which are produced by the animal. So, taking care of animals is the utmost responsibility of human beings. Nowadays, a disease named lumpy skin disease (LSD) is spreading very rapidly amongst cattle and water buffalo. It is an infectious, eruptive, sometimes lethal condition marked by skin nodules. In this paper, we intend to predict whether the cattle of a specific geographic location can have this disease or not in the present or maybe in the future so that preventive measures can be taken accordingly. We have applied various machine learning algorithms to the lumpy skin disease dataset and compared their accuracy in predicting the disease. The dataset consists of 18603 instances and 16 features with target columns having 2 values, 0 for occurrence lumpy disease and 1 for the non-occurrence of lumpy disease. Among all applied algorithms, we have found that the RandomForest algorithm outperforms others with the highest accuracy 97.7%.