## 5 Discussion  
The results of this study demonstrate how different ML techniques, such as Random Forest, DTs, and Linear Regression, can enhance the prediction of fish mortality rates. Among these, the Random Forest model outperformed both DTs and Linear Regression, achieving the lowest MSE, and the highest R\({}^{2}\) value, as shown in Table 8.  
The C-index values indicate the models' ranking accuracy, providing a measure of the model's ability to rank-order outcomes accurately, where Random Forest achieved the highest C-index of 0.85, demonstrating superior predictive performance. DTs and Linear Regression recorded C-index values of 0.65 and 0.70, respectively, highlighting Random Forest's advantage in maintaining prediction consistency across data instances. Although Linear Regression performed adequately when predicting mid-range values, it exhibited significant deviations in extreme data points, while the DTs model performed poorly overall. In addition to prediction accuracy, computational efficiency was a key factor in the assessment. As shown in Table 9, the Random Forest model required the most computational resources, with a CPU time ranging from 100 to 200 s and memory usage between 1200 and 1800 MB. Conversely, DTs and Linear Regression were much more efficient, with Linear Regression being the fastest and most lightweight in terms of CPU and memory usage. These computational insights highlight a trade-off between model accuracy and resource efficiency. While Random Forest provided the most accurate predictions, its resource demands were substantially higher. DTs and Linear Regression, although more computationally efficient, struggled with prediction accuracy, particularly with more complex relationships in the data. Specific environmental factors, such as temperature and water conductivity, significantly impacted fish mortality rates. Higher temperatures were correlated with lower death rates, while optimal water conditions and feeding techniques helped mitigate mortality. Notably, the type of food used also showed a negative correlation with mortality rates, indicating that tailored feeding programs could reduce mortality in aquaculture settings. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**ML Model** & **MSE** & **R\({}^{2}\)** & **RMSE** & **C-Index** \\ \hline Random Forest & 698.54 & 0.63 & 26.43 & 0.85 \\ Decision Tree & 4123.37 & \(-\)1.16 & 64.21 & 0.65 \\ Linear Regression & 2675.84 & 0.312 & 51.73 & 0.70 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Algorithms performance results. \begin{table}
\begin{tabular}{c c c} \hline \hline
**ML Model** & **CPU Time (Seconds)** & **Usage (MB)** \\ \hline Random Forest & 100–200 & 1200–1800 \\ Decision Tree & 30–60 & 600–800 \\ Linear Regression & 15–30 & 200–300 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: CPU and memory comparison. The findings of this study provide valuable insights into enhancing aquaculture management practices, particularly in refining feeding strategies and improving environmental standards [39]. A performance assessment evaluation of several ML models for predicting fish mortality rates in the aquaculture industry revealed that Random Forest, despite its higher computational cost, outperformed the other models. It achieved the lowest MSE and the highest coefficient of determination (R\({}^{2}\)) at 0.63. These performance metrics indicate that the Random Forest model can explain the variance in the data and produce reliable predictions. The analysis also revealed that DTs performed poorly, with an MSE of 4123.37 and an R\({}^{2}\) of \(-\)1.16, indicating their inability to provide reliable predictions. Linear Regression _was more interpretable, but it faced difficulties with_ extreme values. This issue pinpoints the limitations when dealing with non-linearity. Random Forest insights showed that certain factors, including water quality parameters and feeding rates, affected fish mortality rates.