## 2 Materials and Methods  
The patient data used are a dataset from a recent investigation of muscle-specific recovery after cervical spinal cord injury [33]. This retrospective analysis of 748 individuals from the European Multicenter Study about Spinal Cord Injury (NCT01571531) showed associations between corticospinal tract (CST) sparing and upper extremity recovery, improving hand muscle strength recovery prediction. The dataset includes 11 input parameters (clinical and full electrophysiological assessment findings--SSEPs, MEPs, and NCVs) and one output: the final SCI recovery result of 123 patients, who were fully recorded for upper extremity recovery. The input parameters are as follows:  
1. MS: Motor score;
2. DST: Distance from the motor level of injury;
3. LT: Light touch sensation;
4. PP: Pin pick sensation;
5. SSEP_Amp diff_uln: Somatosensory evoked potential amplitude difference ulnar nerve;
6. Hupp_score_SEP: Somatosensory evoked potential score;
7. MEP_Amplitude_abd: Motor evoked potential amplitude recorded from abductor muscle;
8. Hupp_score_MEP: Motor evoked potential score;
9. F-wave persistance uln: F-wave persistence ulnar nerve;
10. Hupp score-NCS: Nerve conduction studies score;
11. REC: muscle strength final recovery (recovery class, no recovery class);
12. AIS: ASIA score {A, B, C, D, E}. Some technical/medical tests are defined as follows: SSEP: Somatosensory evoked potentials (SSEPs) recorded from the central nervous system following electric stimulation of peripheral nerves (upper limb SSEP);  
ASIA: The expanded disability status scale (ASIA) quantifies disability in SCI and monitors changes over time;  
MEP: Motor evoked potentials recorded from peripheral nerves following magnetic stimulation of the central nervous system;  
NCS: Nerve conduction studies using F-wave or compound muscle action potential from the ulnar nerve. The source database can be found at [[https://www.synapse.org/Synapse:syn50900](https://www.synapse.org/Synapse:syn50900)]([https://www.synapse.org/Synapse:syn50900](https://www.synapse.org/Synapse:syn50900)) 348 (accessed on 11 November 2024), created by experts aiming to analyze segmental muscle-specific recovery after cervical spinal cord injury and was extracted from the European Multicenter Study about Spinal Cord Injury as in details described in [33]. Only 123 clinically (MS, DST, LT, PP, and AIS) and \"fully electrophysiologically accessed\" (SSEP, MEP, and NCS in terms of specific test response details and total scoring) patient records were extracted using their coding information and included in our study. The rest of the records were excluded as partly examined or because important information was missing. The final database used in the present approach can also be found at [[https://www.synapse.org/Synapse:syn66044903](https://www.synapse.org/Synapse:syn66044903)]([https://www.synapse.org/Synapse:syn66044903](https://www.synapse.org/Synapse:syn66044903)) (accessed on 6 October 2024). Representative statistics of the included patients' records are presented in Figure 1, where the correlation-based best feature selection revealed that MS, LT, PP, MEP score, and SEP score input parameters are the best predictors for REC and LT and SEP score is the best predictor for AIS [34]. In order to evaluate our hypothesis for patient recovery prediction, we used different ML algorithms and compared their corresponding results based on four metrics commonly used for evaluation of predictions: accuracy, sensitivity, specificity, and precision (abbreviated as Acc, Sen, Spec, and Prec, respectively), defined by the following formulas:  
Figure 1: Database description statistics, according to recovery (**a**) and ASIA (**b**) scores, as well as correlations with clinical (**c**) and electrophysiological (**d**) parameters. LT and SEP scoring are the best attributes for recovery as well as ASIA classification representing the clinical and the electrophysiological evaluations.