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"Research is what I'm doing when I don't know what I'm doing." ~ Wernher von Braun

Fostering Sustainable Aquaculture: Mitigating Fish Mortality Risks Using Decision Trees Classifiers

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The specific application of this work involves the development of an intelligent system for diagnosing and treating fish diseases in Greek fish farming. The project aims to enhance the  competitiveness of Greek fish farming by addressing the increasing mortality rates attributed to unsustainable farming methods and environmental factors. The application of data mining classifiers, particularly decision trees (DTs), in predicting and categorizing fish mortality instances contributes to the development of an intelligent system for disease diagnosis and treatment. The proactive approach, supported by rigorous evaluation processes and a feature importance analysis, holds implications for sustainable aquaculture management and aligns with global sustainability initiatives.


A proposal has been put forward advocating a data-driven strategy that employs classifiers from data mining to foresee and categorize instances of fish mortality. This addresses the increasing concerns regarding the death rates in caged fish environments because of the unsustainable fish farming techniques employed and environmental variables involved. The aim of this research is to enhance the competitiveness of Greek fish farming through the development of an intelligent system that is able to diagnose fish diseases in farms. This system concurrently addresses medication and dosage issues. To achieve this, a comprehensive dataset derived from various aquaculture sources was used, including various factors such as the geographic locations, farming techniques, and indicative parameters such as the water quality, climatic conditions, and fish biological characteristics. The main objective of the research was to categorize fish mortality cases through predictive models. Advanced data mining classification methods, specifically decision trees (DTs), were used for the comparison, aiming to recognize the most appropriate method with high precision and recall rates in predicting fish death rates. To ensure the reliability of the results, a methodical evaluation process was adopted, including cross-validation and a classification performance assessment. In addition, a statistical analysis was performed to gain insights into the factors that identify the correlations between the various factors affecting fish mortality. This analysis contributes to the development of targeted conservation and restoration action strategies. The research results have important implications for sustainable management actions, enabling stakeholders to proactively address issues and monitor aquaculture practices. This proactive approach ensures the protection of farmed fish quantities while meeting global seafood requirements. The data mining using a classification approach coincides with the general context of the UN sustainability goals, reducing the losses in seafood management and production when dealing with the consequences of climate change.

Gkikas D. C., Gkikas M. C., Theodorou J. A. (2024). Fostering Sustainable Aquaculture: Mitigating Fish Mortality Risks Using Decision Trees Classifiers. Applied Sciences. 2024; 14(5):2129. https://doi.org/10.3390/app140521295

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