## 3 Results
Bayesian classifiers, a family of classification algorithms based on Bayes' Theorem, were also employed. The Naive Bayes classifier, one of the simplest and most effective, allows for rapid model development and fast prediction. Naive Bayes is primarily used for classification tasks and is particularly well suited for text classification problems. Its computational efficiency enables swift processing and simplified predictions, even with high-dimensional data. This model estimates the probability that a given instance belongs to a specific class based on a predefined set of features (ASIA score). The dataset was randomly divided (66% records for training, 34% records for testing). Lastly, the random forest algorithm was implemented, building upon the bagging method by incorporating both bagging and feature randomness to create an ensemble of uncorrelated decision trees. Feature randomness ensures low correlation among decision trees by generating a random subset of features. This distinguishes random forest from traditional decision trees, where all potential feature splits are considered; random forest selects only a subset of features for splitting. The random forest algorithm requires setting three hyper parameters before training: node size, the number of trees, and the number of features sampled. Once configured, the classifier can be used for both regression and classification tasks. The model consists of multiple decision trees, each built from a bootstrap sample. Approximately one-third of this sample is reserved as test data (the out-of-bag (OOB) sample), which are later used for validation. Another layer of randomness is introduced through feature bagging, enhancing dataset diversity and reducing correlation among decision trees. The prediction process varies based on the problem type: for regression tasks, individual decision tree outputs are averaged, while for classification tasks, the final class is determined through majority voting. The OOB sample serves as a form of cross-validation, ensuring a reliable prediction outcome. We tested and present here different algorithm performances to (a) strengthen our hypothesis about electrophysiological assessment and (b) provide different alternatives, using a simpler model, such as Naive Bayes, or a more interpretable one, such as J48, instead of random forest or ensemble models. This comparison is very helpful, especially considering the balance between performance, interpretability, and computational requirements of each approach [36]. Patient records were divided into training and test sets randomly. The performance of the machine learning models that were developed using the training set was evaluated using accuracy, precision, recall, F1-score, and area under the receiver operating characteristic (ROC) curve (AUC-ROC) in the test set. Part of the results of the models on the testing set is shown in Figure 2 and Table 1. We calculated the prediction accuracy of motor recovery and current ASIA score according to all input parameters. Possible electrophysiology biomarkers (SEPs, MEPs, and all NCSs data) were also compared with clinical evaluation results. All results reveal the importance of SEPs as predictors of motor recovery and ASIA score in SCI patients. Specific comparisons between SEP, MEP, NCS, and clinical assessment accuracies are detailed here, referencing Table 1 and Figure 3. Electrophysiology assessment, when available, elevates the total accuracy from only clinical investigation to 93.1% (from at most 75.6%) for final motor recovery prediction and to 89% (from at most 66.3%) for ASIA score determination. More data are needed to confirm these results (Table 1). We suggest that sensory electrophysiology assessment (including SEP and NCV data) is a faster, less expensive, portable, and simpler alternative to other prognostic tests and more effective than clinical assessment methods, such as AIS or ICF scores.