Assessing machine learning techniques in forecasting lumpy skin disease occurrence based on meteorological and geospatial features.

ABSTRACT: Lumpy skin disease virus (LSDV) causes an infectious disease in cattle. Due to its direct relationship with the survival of arthropod vectors, geospatial and climatic features play a vital role in the epidemiology of the disease. The objective of this study was to assess the ability of some machine learning algorithms to forecast the occurrence of LSDV infection based on meteorological and geological attributes. Initially, ExtraTreesClassifier algorithm was used to select the important predictive features in forecasting the disease occurrence in unseen (test) data among meteorological, animal population density, dominant land cover, and elevation attributes. Some machine learning techniques revealed high accuracy in predicting the LSDV occurrence in test data (up to 97%). In terms of area

SUBMITTER: Afshari Safavi E 

PROVIDER: S-EPMC8759057 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature