ORIGINAL RESEARCH article
Front. Vet. Sci.
Sec. Veterinary Pharmacology and Toxicology
Volume 12 - 2025 | doi: 10.3389/fvets.2025.1652115
This article is part of the Research TopicUnderstanding Anaesthetic Effects on Aquatic AnimalsView all 4 articles
Using Machine Learning to Predict Anaesthetic Dose in Fish: A Case Study Using Nutmeg Oil
Provisionally accepted
- Recep Tayyip Erdoğan University, Rize, Türkiye
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Application of anesthetic chemicals in aquaculture is important to minimize stress under normal operations such as handling, transport, and artificial breeding. In the past decade, the preference for natural anesthetics over synthetic ones has increased due to welfare issues regarding fish welfare and food safety. This study investigates the anesthetic efficacy of nutmeg oil (Myristica fragrans) in three freshwater fish species -Cyprinus carpio (Common carp), Acipenser gueldenstaedtii (Danube sturgeon), and Oncorhynchus mykiss (Rainbow trout)by modeling behavioral (Induction and recovery times) and hematological responses using artificial neural networks (ANNs). Experimental data obtained from previous studies were used to develop feedforward ANN models for each species and parameter. Each model was trained using different activation functions (purelin, tansig, logsig) and optimization algorithms (traingda, trainrp, trains), and the optimal network architecture was selected based on prediction performance for each output variable. The ANN models successfully predicted species-specific responses, revealing distinct sensitivity levels to nutmeg oil. Model performance was assessed using R², RMSE, and MAPE metrics, and the results revealed strong predictive capabilities of the ANN models across different fish species and physiological parameters.The most accurate models were obtained for WBC across all species, while induction and recovery times varied depending on fish physiology. The study demonstrates that ANN-based modeling can be a powerful tool for predicting optimal anesthetic doses and physiological responses without additional invasive testing. The results provide a scientific foundation for developing species-specific, welfare-limited anesthetic protocols and indicate the potential of artificial intelligence applications to experimental aquaculture practices.
Keywords: Myristica fragrans, Fish anesthesia, artificial neural network, hematological parameters, Species sensitivity
Received: 23 Jun 2025; Accepted: 04 Aug 2025.
Copyright: © 2025 Minaz, Alparslan and Er. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Mert Minaz, Recep Tayyip Erdoğan University, Rize, Türkiye
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