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This review emphasizes the urgent need for effective diagnostic strategies for African swine fever (ASF), a serious disease affecting pig populations worldwide. The aim of the review is to analyze the existing research on ASF diagnostics through a comprehensive literature review, focusing on different diagnostic approaches, including clinical assessments, PCR tests, ELISA, rapid tests and epidemiological models. It examines their sensitivity, specificity and overall performance, addressing challenges such as the varying sensitivity of tests and cross-reactivity. The review highlights both the strengths and limitations of current methods and suggests areas for improvement and standardization. Recommendations are made for future research and the development of innovative diagnostic tools to improve ASF surveillance and control. This study makes a practical contribution by providing a detailed assessment of ASF diagnostic methods from which veterinary scientists and practitioners can benefit. Theoretical contributions include the identification of gaps in ASF diagnostics and the refinement of discussions on diagnostic accuracy and reliability. These findings are consistent with the journal’s focus on infectious diseases and veterinary research and support progress in veterinary medicine and animal welfare.

This study focused on improving the diagnosis of African swine fever (ASF) by improving accuracy, reliability and precision using various machine learning algorithms. Data from the European Union Reference Laboratory for ASF and the EU Animal Disease Information System (ADIS) containing clinical information on wild and domestic pigs were processed and converted into numerical and categorical formats for analysis. Various machine learning models were tested as part of the study, including linear regression, Bayesian regression, support vector machines (SVM), decision trees, random forest classifiers, artificial neural networks (ANNs) and logistic regression. The models were evaluated using metrics such as accuracy, precision, recall, F1-score and root mean square error (RMSE). The results showed that the SVM achieved the lowest performance with 44.4% accuracy, followed by the logistic regression model with 61.5%. The neural network model achieved 69% accuracy, closely followed by the decision tree model with 70%. The linear regression model performed slightly better with 74% accuracy. Bayesian regression achieved a higher accuracy of 80%, while the Random Forest model outperformed all others, achieving the highest accuracy of 88.9%. These results make an important contribution to economic efficiency and innovative applications in veterinary medicine and improve disease management through advanced machine learning techniques.