## 4 Results and Discussion
### The Results of the ADI Prediction Models
#### 4.1.1 The Evaluation Parameters of the ML- and DL-Based ADI Models  
The evaluation parameters of the SVM-, RF-, LSTM-, and CNN-based ADI prediction models are given in Table 16. The model uses a 5-fold cross-validation approach. The learning rate is maintained at 0.001. The evaluation parameters are measured in two scenarios,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **HGB Classifier 1** & **HGB Classifier 2** & **HGB Classifier 3** \\ \hline Scaler & Min-Max Scaler & Min-Max Scaler & Min-Max Scaler \\ \hline Dataset shuffled & Yes & Yes & Yes \\ \hline Loss & Log loss & Log loss & Log loss \\ \hline Maximum iteration & 125 & 150 & 175 \\ \hline Maximum leaf nodes & 20 & 25 & 30 \\ \hline Minimum sample leaf & 10 & 15 & 20 \\ \hline L2 regularization & 0.2 & 0.15 & 0.25 \\ \hline Maximum bins & 127 & 255 & 127 \\ \hline Early slopping & Auto & Bool & Auto \\ \hline Validation fraction & 0.2 & 0.15 & 0.15 \\ \hline Tolerance & 0.001 & 0.002 & 0.0025 \\ \hline Average & Macro-average & Macro-average & Macro-average \\ \hline Learning rate & 0.001–0.048 & 0.001–0.048 & 0.001–0.048 \\ \hline \hline \end{tabular}
\end{table}
Table 15: The parameters of the three different HGB models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **SGD Model 1** & **SGD Model 2** & **SGD Model 3** \\ \hline Scaler & Standard Scaler & Standard Scaler & Standard Scaler \\ \hline Dataset shuffled & Yes & Yes & Yes \\ \hline Validation fraction & 0.03 & 0.04 & 0.02 \\ \hline Verbose & 0.02 & 0.03 & 0.04 \\ \hline Tolerance & 0.002 & 0.001 & 0.003 \\ \hline Fit Intercept & True & False & True \\ \hline Alpha & 0.004 & 0.002 & 0.003 \\ \hline Penalty & L2 & L1 & L2 \\ \hline Loss & Log loss & Log loss & Log loss \\ \hline Maximum iterations & 900 & 800 & 750 \\ \hline Kernel & Linear & Linear & Linear \\ \hline Average & Macro-average & Macro-average & Macro-average \\ \hline Learning rate & 0.001–0.048 & 0.001–0.048 & 0.001–0.048 \\ \hline \hline \end{tabular}
\end{table}
Table 14: The parameters of the three different SGD models.  
which are the training dataset and the test dataset. Also, the evaluation parameters are compared with the literature.  
Among the models tested, convolutional neural networks (CNNs) demonstrated strong generalization capabilities, with the CNN [17] achieving the highest test accuracy (0.977), though detailed performance metrics were not provided. Random Forest models (M1 and M2) achieved excellent F1 scores near 0.59. Support Vector Machines (SVMs) with radial basis function (RBF) and polynomial kernels performed well, showing a good balance between accuracy and generalization. In contrast, linear and sigmoid SVMs performed poorly across all metrics. Long Short-Term Memory (LSTM) models showed moderate performance, with relatively low F1 scores, indicating challenges in precision and recall despite their ability to handle sequential data. Overall, CNNs and Random Forests emerged as the most effective models, with SVMs offering a balanced alternative, while LSTM and sigmoid-based approaches were less suitable without further optimization.