## 3 Results  
For better interpretation of our hypothesis about the importance of electrophysiology assessment as an SCI patient recovery predictor, different ML algorithms were used. The main approach was ensemble learning, a machine learning technique combining multiple models (regression models, neural networks, and decision trees) to enhance predictive accuracy. This approach integrates several individual models to achieve better results than a single model alone [36]. Research has validated the effectiveness of ensemble learning in machine learning and convolutional neural networks (CNNs). Each machine learning model is influenced by factors including training data, hyper parameters, and other parameters, impacting the total error. Even with the same training algorithm, different models emerge with distinct levels of bias, variance, and irreducible error. Ensemble methods minimize overall error by merging multiple diverse models, preserving each model's strengths. Studies indicate that ensembles with greater diversity yield more accurate predictions. Ensemble learning effectively mitigates over fitting without significantly increasing model bias. Ensembles of diverse, under-regularized models can outperform individual regularized models [35]. Ensemble techniques can also address challenges related to high-dimensional data. In this study, ensemble algorithms, like vote (integrating decision trees (J48), artificial neural networks (multilayer perceptron), and Bayes (Naive Bayes)) and random forest, were used. All models were implemented using the Waikato Environment for Knowledge Analysis (WEKA) platform [37]. WEKA is a widely used of machine learning and data analysis free software licensed under the GNU General Public License. It was developed at the University Of Waikato, New Zealand. WEKA version 3.9.0 provides visualization tools and machine learning algorithms for data analysis and predictive modeling. It features graphical user interfaces and data preprocessing functions. WEKA integrates various artifi  
Figure 2: Decision tree examples resulting from J48 algorithm for SSEPs Motor recovery (**a**) and ASIA score (**b**), predictions including statistical classification results in terms of precision, sensitivity, specificity, and ROC area for each output class and the weighted performance of each model. cial intelligence techniques and statistical methods, supporting core data mining processes. The platform operates on the principle that data are presented in a structured format, where each instance consists of a defined set of attributes. Many of WEKA's standard machine learning algorithms generate decision trees for classification tasks. The first approach was decision trees using the J48 algorithm. Decision trees extract insights and create predictive models. A decision tree is structured like a flowchart, systematically dividing data into branches without information loss. It serves as a hierarchical sorting mechanism, predicting outcomes based on sequential decision-making steps. The tree construction process follows a structured methodology: each node represents a decision point based on a specific parameter, determining the progression to the next branch. This iterative process continues until a leaf node is reached, representing the final predicted outcome (ASIA prediction). To assess the accuracy of the constructed decision tree, the random tree algorithm was used to generate the model based on the dataset. The second approach was WEKA's Neural Network (multilayer perceptron with a hidden layer), trained using the error back-propagation algorithm. This intelligent system followed an I-H-O (input-hidden layers-output layers) format [32]. The training process involved gradually increasing the number of neurons in the hidden layer and extending the number of training epochs. With a constant learning rate, a consistent reduction in error per training epoch and improved classification performance were observed. Optimal results were obtained with seven hidden neurons and 15,000 training epochs.