Development of machine learning models to predict clinical outcome and recovery time in dogs with parvovirus enteritis
Canine parvovirus (CPV) is one of the most contagious viral diseases in dogs that usually presents with diarrhea, vomiting, and fever. Various clinical and laboratory biomarkers such as SIRS, leukopenia, neutropenia and CRP have been introduced to predict the final outcome of dogs with CPV. With the...
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Veterinary Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fvets.2025.1555714/full |
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| author | Negin Sanaei Mohamad Zamani-Ahmadmahmudi Seyed Mahdi Nassiri |
| author_facet | Negin Sanaei Mohamad Zamani-Ahmadmahmudi Seyed Mahdi Nassiri |
| author_sort | Negin Sanaei |
| collection | DOAJ |
| description | Canine parvovirus (CPV) is one of the most contagious viral diseases in dogs that usually presents with diarrhea, vomiting, and fever. Various clinical and laboratory biomarkers such as SIRS, leukopenia, neutropenia and CRP have been introduced to predict the final outcome of dogs with CPV. With the advent of machine learning methods/algorithms, various models can be developed using a combination of clinical and non-clinical variables to predict clinical outcome in different diseases with higher efficiency compared to traditional biomarkers. In this study, we sought to develop models to predict clinical outcome and recovery time in dogs with CPV infection using 10 and 4 machine learning algorithms, respectively. A model was developed using four variables (SIRS, deworming, vaccination and crying) to predict clinical outcome. The performance of this model was measured using three metrics: accuracy scores, AUC (area under the Receiver Operating Characteristic (ROC) curve) and AUC score. Another model was constructed using five variables (retching, foul smelling, housing, dehydration, and shift-to-left) to estimate recovery time. The performance of this model was evaluated using two criteria: mean square error (MSE) and root mean square error (RMSE). In the model developed for clinical outcome, the average of accuracy scores, AUC scores and AUCs in the test dataset were 0.84, 0.90 and 0.73, respectively. The second model predicted the recovery time in the test group with a mean error of 2 days (RMSE = 2.05). Our findings demonstrate that ML models can effectively integrate clinical and laboratory features to predict survival and recovery time in CPV-infected dogs, offering a valuable tool for early prognosis and treatment optimization. |
| format | Article |
| id | doaj-art-87bf20cdfe764de38d9c6439c8ad6c77 |
| institution | OA Journals |
| issn | 2297-1769 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Veterinary Science |
| spelling | doaj-art-87bf20cdfe764de38d9c6439c8ad6c772025-08-20T02:12:23ZengFrontiers Media S.A.Frontiers in Veterinary Science2297-17692025-04-011210.3389/fvets.2025.15557141555714Development of machine learning models to predict clinical outcome and recovery time in dogs with parvovirus enteritisNegin Sanaei0Mohamad Zamani-Ahmadmahmudi1Seyed Mahdi Nassiri2Department of Clinical Pathology, Faculty of Veterinary Medicine, University of Tehran, Tehran, IranDepartment of Clinical Science, Faculty of Veterinary Medicine, Shahid Bahonar University of Kerman, Kerman, IranDepartment of Clinical Pathology, Faculty of Veterinary Medicine, University of Tehran, Tehran, IranCanine parvovirus (CPV) is one of the most contagious viral diseases in dogs that usually presents with diarrhea, vomiting, and fever. Various clinical and laboratory biomarkers such as SIRS, leukopenia, neutropenia and CRP have been introduced to predict the final outcome of dogs with CPV. With the advent of machine learning methods/algorithms, various models can be developed using a combination of clinical and non-clinical variables to predict clinical outcome in different diseases with higher efficiency compared to traditional biomarkers. In this study, we sought to develop models to predict clinical outcome and recovery time in dogs with CPV infection using 10 and 4 machine learning algorithms, respectively. A model was developed using four variables (SIRS, deworming, vaccination and crying) to predict clinical outcome. The performance of this model was measured using three metrics: accuracy scores, AUC (area under the Receiver Operating Characteristic (ROC) curve) and AUC score. Another model was constructed using five variables (retching, foul smelling, housing, dehydration, and shift-to-left) to estimate recovery time. The performance of this model was evaluated using two criteria: mean square error (MSE) and root mean square error (RMSE). In the model developed for clinical outcome, the average of accuracy scores, AUC scores and AUCs in the test dataset were 0.84, 0.90 and 0.73, respectively. The second model predicted the recovery time in the test group with a mean error of 2 days (RMSE = 2.05). Our findings demonstrate that ML models can effectively integrate clinical and laboratory features to predict survival and recovery time in CPV-infected dogs, offering a valuable tool for early prognosis and treatment optimization.https://www.frontiersin.org/articles/10.3389/fvets.2025.1555714/fullcanine parvovirusmachine learningsurvivalpredictiondog |
| spellingShingle | Negin Sanaei Mohamad Zamani-Ahmadmahmudi Seyed Mahdi Nassiri Development of machine learning models to predict clinical outcome and recovery time in dogs with parvovirus enteritis Frontiers in Veterinary Science canine parvovirus machine learning survival prediction dog |
| title | Development of machine learning models to predict clinical outcome and recovery time in dogs with parvovirus enteritis |
| title_full | Development of machine learning models to predict clinical outcome and recovery time in dogs with parvovirus enteritis |
| title_fullStr | Development of machine learning models to predict clinical outcome and recovery time in dogs with parvovirus enteritis |
| title_full_unstemmed | Development of machine learning models to predict clinical outcome and recovery time in dogs with parvovirus enteritis |
| title_short | Development of machine learning models to predict clinical outcome and recovery time in dogs with parvovirus enteritis |
| title_sort | development of machine learning models to predict clinical outcome and recovery time in dogs with parvovirus enteritis |
| topic | canine parvovirus machine learning survival prediction dog |
| url | https://www.frontiersin.org/articles/10.3389/fvets.2025.1555714/full |
| work_keys_str_mv | AT neginsanaei developmentofmachinelearningmodelstopredictclinicaloutcomeandrecoverytimeindogswithparvovirusenteritis AT mohamadzamaniahmadmahmudi developmentofmachinelearningmodelstopredictclinicaloutcomeandrecoverytimeindogswithparvovirusenteritis AT seyedmahdinassiri developmentofmachinelearningmodelstopredictclinicaloutcomeandrecoverytimeindogswithparvovirusenteritis |