## 4 Discussion  
Patients with SCI often undergo electrophysiological assessments (MEPs and NCSs) to evaluate neurological deficits and predict recovery potential. Studies have shown significant correlations between electrodiagnostic findings and ASIA scores, with lower scores associated with more severe impairments [30]. Specific electrophysiological parameters enhance the predictive accuracy of ASIA assessments regarding recovery outcomes. Electrophysiological evaluations identify preserved neural pathways, informing rehabilitation strategies and contributing to improved functional recovery [38]. However, relying solely on ASIA scores might overlook subtle nuances of potential recovery, suggesting the need for a more integrated evaluation approach. In this study, we applied different machine learning algorithms to analyze a dataset consisting of clinical and electrophysiological parameters from individuals with spinal cord injury. Our goal was to evaluate the ability of these models to predict two key outcomes: motor recovery and ASIA impairment classification. Among the models tested, random forest demonstrated the highest accuracy, achieving 93.1% for motor recovery and 89.0% for ASIA classification. These results highlight the potential of combining electrophysiological  
Figure 3: Performing machine learning, selected models predicting outcomes for spinal cord injury (SCI) patients, accuracy varies across biomarkers. Somatosensory evoked potentials (SEPs) and nerve conduction studies (NCSs) are the most effective predictors, showing similar high performance levels. markers, such as MEP and SEP, with machine learning techniques to improve prognostic assessments in SCI. While random forest achieved the highest accuracy in both motor recovery (93.1%) and ASIA classification (89.0%), other models such as Naive Bayes (82.8%, 79.3%) and J48 (86.2%, 82.7%) also performed competitively. These simpler algorithms offer practical advantages: Naive Bayes is computationally efficient and robust with small datasets, while J48 provides interpretable decision trees that can aid clinicians in understanding and communicating predictive outcomes. In scenarios where model transparency or processing speed is critical--such as real-time clinical decision-making or use in low-resource settings--these models may be preferable despite slightly lower accuracy. Our research demonstrates that SSEPs have significant prognostic value in predicting motor recovery in SCI patients. Our findings indicate a positive correlation between SSEP latencies and motor recovery, further substantiated by their association with the ASIA impairment scale (AIS). These findings align with existing literature suggesting that SSEPs and MEPs are more sensitive in detecting disease progression than conventional clinical assessments while also offering cost-effective and time-efficient advantages [39]. The integration of machine learning algorithms into SCI prognostic modeling further refines predictive accuracy. By analyzing large-scale datasets, these algorithms can identify complex patterns not immediately discernible to clinicians. Existing rehabilitation protocols could be enhanced with these predictors to create personalized therapy plans. For instance, patients with preserved MEPs may benefit from intensive motor training earlier in the rehabilitation process, while those lacking such responses might be prioritized for compensatory strategies or neuromodulation trials. This approach can support more targeted interventions, optimize resource allocation, and ultimately enhance functional outcomes. Such advancements optimize the decision-making process and enable evidence-based decisions informed by real-time data analysis. Predictive analytics facilitates early intervention and timely modifications in treatment plans. As these technological innovations progress, they have the potential to transform the management of chronic neurological conditions, paving the way for more personalized, precise, and effective treatment pathways.