Peer-reviewed veterinary case report
Machine learning predicts structural epilepsy in dogs with seizures
By Thomas Flegel et al.·Published in Frontiers in Veterinary Science·2024·Department for Small Animals, Veterinary Faculty, Leipzig University, Leipzig, Germany, CH·View original on DOAJ →
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Original publication title: Machine learning algorithms predict canine structural epilepsy with high accuracy
- Species:
- dog
Plain-English summary
A group of dogs with a history of seizures was studied to see if machine learning could help predict structural epilepsy, a condition that can cause ongoing seizure issues. The study found that certain factors, like the dog's age at their first seizure and how often they had seizures in the past, could accurately indicate whether a dog had structural epilepsy. The algorithms used in the study were able to predict this condition with over 96% accuracy. This could help veterinarians and pet owners make better decisions about treatment and management for dogs experiencing seizures.
People also search for: dog seizures causes · how to treat structural epilepsy in dogs · predicting seizures in dogs
Abstract
IntroductionClinical reasoning in veterinary medicine is often based on clinicians’ personal experience in combination with information derived from publications describing cohorts of patients. Studies on the use of scientific methods for patient individual decision making are largely lacking. This applies to the prediction of the individual underlying pathology in seizuring dogs as well. The aim of this study was to apply machine learning to the prediction of the risk of structural epilepsy in dogs with seizures.Materials and methodsDogs with a history of seizures were retrospectively as well as prospectively included. Data about clinical history, neurological examination, diagnostic tests performed as well as the final diagnosis were collected. For data analysis, the Bayesian Network and Random Forest algorithms were used. A total of 33 features for Random Forest and 17 for Bayesian Network were available for analysis. The following four feature selection methods were applied to select features for further analysis: Permutation Importance, Forward Selection, Random Selection and Expert Opinion. The two algorithms Bayesian Network and Random Forest were trained to predict structural epilepsy using the selected features.ResultsA total of 328 dogs of 119 different breeds were identified retrospectively between January 2017 and June 2021, of which 33.2% were diagnosed with structural epilepsy. An overall of 89,848 models were trained. The Bayesian Network in combination with the Random feature selection performed best. It was able to predict structural epilepsy with an accuracy of 0.969 (sensitivity: 0.857, specificity: 1.000) among all dogs with seizures using the following features: age at first seizure, cluster seizures, seizure in last 24 h, seizure in last 6 month, and seizure in last year.ConclusionMachine learning algorithms such as Bayesian Networks and Random Forests identify dogs with structural epilepsy with a high sensitivity and specificity. This information could provide some guidance to clinicians and pet owners in their clinical decision-making process.
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Search related cases →Original publication on DOAJ: https://doi.org/10.3389/fvets.2024.1406107