In Journal of veterinary internal medicine ; h5-index 37.0
BACKGROUND : Central nervous system (CNS) infections in cattle are a major cause of economic loss and mortality. Machine learning (ML) techniques are gaining widespread application in solving predictive tasks in both human and veterinary medicine.
OBJECTIVES : Our primary aim was to develop and compare ML models that could predict the likelihood of a CNS disorder of infectious or inflammatory origin in neurologically-impaired cattle. Our secondary aim was to create a user-friendly web application based on the ML model for the diagnosis of infection and inflammation of the CNS.
ANIMALS : Ninety-eight cattle with CNS infection and 86 with CNS disorders of other origin.
METHODS : Retrospective observational study. Six different ML methods (logistic regression [LR]; support vector machine [SVM]; random forest [RF]; multilayer perceptron [MLP]; K-nearest neighbors [KNN]; gradient boosting [GB]) were compared for their ability to predict whether an infectious or inflammatory disease was present based on demographics, neurological examination findings, and cerebrospinal fluid (CSF) analysis.
RESULTS : All 6 methods had high prediction accuracy (≥80%). The accuracy of the LR model was significantly higher (0.843 ± 0.005; receiver operating characteristic [ROC] curve 0.907 ± 0.005 $$ 0.907\pm 0.005 $$ ) than the other models and was selected for implementation in a web application.
CONCLUSION AND CLINICAL IMPORTANCE : Our findings support the use of ML algorithms as promising tools for veterinarians to improve diagnosis. The open-access web application may aid clinicians in achieving correct diagnosis of infectious and inflammatory neurological disorders in livestock, with the added benefit of promoting appropriate use of antimicrobials.
Ferrini Sara, Rollo Cesare, Bellino Claudio, Borriello Giuliano, Cagnotti Giulia, Corona Cristiano, Di Muro Giorgia, Giacobini Mario, Iulini Barbara, D’Angelo Antonio
2023-Mar-10
bovine neurology, central nervous system infections, clinical decision-making process, machine learning
