Machine learning model for predicting Peste des Petits Ruminants
| dc.contributor.author | Nyambo, Devotha | |
| dc.contributor.author | Ngulumbi, Nguse | |
| dc.contributor.author | Mduma, Neema | |
| dc.contributor.author | Sinde, Ramadhani | |
| dc.contributor.author | Lyimo, Tumaini | |
| dc.date.accessioned | 2024-06-06T10:50:24Z | |
| dc.date.available | 2024-06-06T10:50:24Z | |
| dc.date.issued | 2023-11-16 | |
| dc.description | This research article was published by IEEE | en_US |
| dc.description.abstract | Peste des petits ruminants (PPR) is a viral disease that affects small ruminants and is prevalent in many developing countries, particularly in Africa and Asia. It can spread through direct contact, air, and contaminated feed and water. PPR can result in significant economic losses and has a detrimental impact on small ruminant production and trade. Clinical signs include fever, respiratory distress, and diarrhoea, and prevention is primarily through vaccination with a live attenuated vaccine. In this study, 24 samples were selected, pre-processed and synthesized using the Conditional Tabular Generative Adversarial Networks (CTGAN) model. Feature extraction was performed, revealing difficult_breathing as the most important feature in predicting PPR in ruminants. The study used Random Forest Classifier which was fine-tuned using Bayesian Optimization to attain an accuracy of 91%. | en_US |
| dc.identifier.uri | https://doi.org/10.1109/AAIAC60008.2023.10465342 | |
| dc.identifier.uri | https://dspace.nm-aist.ac.tz/handle/20.500.12479/2714 | |
| dc.language.iso | en | en_US |
| dc.publisher | IEEE | en_US |
| dc.subject | Livestock health | en_US |
| dc.subject | PPR disease | en_US |
| dc.subject | Machine Learning | en_US |
| dc.subject | Prediction | en_US |
| dc.title | Machine learning model for predicting Peste des Petits Ruminants | en_US |
| dc.type | Article | en_US |