# Fostering Sustainable Aquaculture: Mitigating Fish Mortality Risks Using Decision Trees Classifiers
## 6 Conclusions  
The research proposes a data-oriented strategy deploying classification methods to forecast and categorize caged fish mortality examples, indicating the increasing rates attributed to unsustainable fish farming and environmental factors. The current research aims to support Greek fish farming competitiveness by developing an intelligent system that will eventually allow for fish diseases diagnosis, emphasizing medication and dosage issues. The project utilizes a comprehensive dataset, enabling predictive modeling with state-of-the-art data mining classifiers, particularly DTs, ensuring high precision and recall rates. The feature importance analysis offers insights for developing targeted conservation strategies.  
The DT classifier, trained on extensive datasets from the Ionian Sea, showcases a robust predictive performance in fish mortality instances. The correlation findings highlight factors influencing mortality, such as the median atomic weight, the volume of the cell, the concentration of fish, and the water temperature. The DT structure provides thresholds for feature variables, contributing to the classification of outcomes. Pruning, model evaluation using the 10-fold cross-validation, and high accuracies (95.47%, 95.43%, 96.26%) underscore the model's reliability. The discussion emphasizes the model's effectiveness, the data-driven approaches in cage aquaculture management, and implications for sustainable practices.  
The conclusions also consider the future directions and global seafood demand, emphasizing the research's importance in addressing complex challenges in cage aquaculture. The project's promising direction for improving fish farming practices, coupled with the necessity for endless research and collaboration to meet global seafood demands sustainably, is highlighted. The study provides valuable new knowledge for establishing the development of innovative actions and evidence-oriented practices to optimize the resilience and sustainability of the aquaculture industry.  
This research pinpoints the keystone role of data-oriented approaches and methods in predicting caged fish mortality rates. By designing, training, and applying advanced data mining classifiers, this research not only demonstrates the potential effectiveness of such models but also sets a milestone for future efforts in the field. The findings manage to explain the broader goal of sustainable cage aquaculture management, providing a beacon for efforts to meet the growing global demand for seafood without compromising sustainability.  
Conceptualization, D.C.G.; methodology, D.C.G.; software, D.C.G. and M.C.G.; validation, D.C.G. and M.C.G.; formal analysis, D.C.G. and M.C.G.; investigation, D.C.G. and M.C.G.; resources, J.A.T.; data curation, D.C.G. and M.C.G.; visualization, D.C.G. and M.C.G.; writing--original draft, D.C.G.; writing--review and editing, D.C.G., M.C.G. and J.A.T.; supervision, D.C.G.; project administration, J.A.T.; funding acquisition, J.A.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the OPERATIONAL PROGRAM FOR FISHERIES and MARTIME 2014-2020, grant number (MIS) 5067321.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to copy right restrictions from the private company that owns it.  
This work is supported by the action \"Improving Competitiveness of the Greek Fish Farming through Development of Intelligent Systems for Disease Diagnosis & Treatment Proposal and Relevant Risk Management Supporting Actions\".  
Conflicts of Interest: Author [PERSON] is the founder and owner of the company OWEB Digital Experience. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.