# Ensemble Modelling for Predicting Fish Mortality +
###### Abstract
Footnote †: These authors contributed equally to this work. Footnote †: This article is a substantial extension and elaboration of previous work, appeared in the Proceedings of the 14 th International Conference on Information, Intelligence, Systems and Applications (IISA 2023), Volos, Greece, 10–12 July 2023. Footnote †: These authors contributed equally to this work. Footnote †: This article is a substantial extension and elaboration of previous work, appeared in the Proceedings of the 14 th International Conference on Information, Intelligence, Systems and Applications (IISA 2023), Volos, Greece, 10–12 July 2023. Footnote †: These authors contributed equally to this work. Footnote †: This article is a substantial extension and elaboration of previous work, appeared in the Proceedings of the 14 th International Conference on Information, Intelligence, Systems and Applications (IISA 2023), Volos, Greece, 10–12 July 2023. Footnote †: These authors contributed equally to this work. Footnote †: This article is a substantial extension and elaboration of previous work, appeared in the 14 th International Conference on Information, Intelligence, Systems and Applications (IISA 2023), Volos, Greece, 10–12 July 2023. Footnote †: These authors contributed equally to this work. Footnote †: This article is a substantial extension and elaboration of previous work, appeared in the 14 th International Conference on Information, Intelligence, Systems and Applications (IISA 2023), Volos, Greece, 10–12 July 2023. Footnote †: These authors contributed equally to this work. Footnote †: This article is a substantial extension and elaboration of previous work, appeared in the Proceedings of the 14 th International Conference on Information, Intelligence, Systems and Applications (IISA 2023), Volos, Greece,context due to their ability to learn non-linear patterns and interactions in large, complex datasets, acting as universal function approximators [3; 4; 5; 6; 7]. Yet, despite their predictive prowess, ANNs often act as \"black boxes\", offering little insight into the relationships between the variables used in predictions. On the other hand, Symbolic Regression (SR) offers interpretable models that can elucidate the underlying structure of data by producing symbolic equations [8; 9] (The higher interpretability of SR has made it a favourable approach used for extracting the underlying laws of physical systems from experimental data [8; 9]). In a similar vein, Decision Trees (DTs), rooted in foundational works by [PERSON] et al. [10], provide a methodical decomposition of the data space into decision nodes and leaves, enabling the straightforward interpretation of the decision paths, which enhances transparency and aids in understanding the feature influence on predictions. Unfortunately, however, the high interpretability of SR and DTs often comes at the expense of prediction performance, particularly when dealing with complex, non-linear relationships. Against this background, in this work, we introduce an ensemble approach that integrates the distinct advantages of ANNs, SR, and DTs. The proposed method not only leverages the high predictive accuracy of ANNs but also combines the interpretability of SR, with the structured decision-making capabilities of DTs. Developed in two stages (intermediate and final), our ensemble method achieves robust results with respect to fish mortality due to infectious diseases and also reveals meaningful relationships among key influencing factors. This article extends our recent previous work published in [11], where standalone ANN and SR models were developed using real-world datasets from the Greek aquaculture industry, specifically tailored to fish diseases caused by the bacteria _Pasteurella_, _Vibrio Harveyi_ and _Myxobacteria_. Building on [11], we present the architecture and evaluation of our innovative ensemble models, each developed for a specific disease.