This paper proposes a novel ensemble approach, integrating Artificial Neural Networks (ANNs), Symbolic Regression (SR), and Decision Trees (DTs), to predict fish mortality caused by infectious diseases. The intensifying global burden of fish diseases threatens the sustainability of aquatic ecosystems and the aquaculture industry, necessitating sophisticated modelling strategies for effective disease management and control. The proposed approach capitalizes on the non-linear data modelling strength of ANNs, the explanatory power of SR, and the decision-making efficiency of DTs, offering both predictive accuracy and interpretable insights. The architecture of the proposed ensemble method is developed in two stages. In the intermediate stage, an ANN is employed to learn the complex, non-linear interactions between various biological and environmental factors impacting fish health. Additionally, SR is applied to produce a symbolic equation that effectively maps the input variables to fish mortality rates. In the final stage, a DT model is included to enhance prediction performance by capturing decision rules from the data. This hybrid approach offers superior prediction performance while also revealing meaningful biological/environmental relationships that can guide preventive and reactive interventions in the management of fish health. We evaluate the developed models using extensive real-world datasets acquired from two large Greek fish-farming units, which encompass representative disease types. The results demonstrate that our ensemble approach significantly outperforms traditional standalone models developed in our recent previous work, achieving enhanced predictive accuracy, robustness, and interpretability. Overall, this research has far-reaching implications for improving disease predictions, facilitating optimal decision-making in aquaculture management, and contributing to the sustainability of global fish stocks.

Farmed fish disease diagnosis is an important problem in the fish farming industry, affecting quality of production and financial losses. In this paper, we present a web-based intelligent system that tackles the problem of fish disease diagnosis. To this end, it uses multiple knowledge representation and reasoning methods: rule-based, case-based, weight-based, and voting. Knowledge, which concerns the diagnosis of sea bass diseases, was acquired from experts in the field and represented in the form of decision trees. The diagnostic process is performed in two stages: a general one and a specialized one. In the general stage, a level-based diagnosis is performed, where environmental parameters, external signs, and internal signs are successively examined, and the three most probable diseases are identified. In the specialized stage, which is optional, a specialized expert system is used for each of the resulting diseases, where additional parameters concerning laboratory tests (microbiological, microscopic, molecular, and chemical) are considered. The general stage is the most useful, given that it can be performed on-site in real-time, whereas the specialized one requires time-consuming lab tests. The system also provides explanations for its decisions. Evaluation of the general-stage diagnostic process showed a top-3 accuracy of 78.79% on expert test cases and 94% on an artificial dataset.