<p>Disorders of consciousness (DoC), including unresponsive wakefulness syndrome (UWS/VS) and minimally conscious state (MCS), pose significant diagnostic challenges due to their complexity and high misdiagnosis rates. This study investigates the prognostic potential of heart rate variability (HRV) ratios between stimulation and baseline, combined with support vector machine (SVM) classification, to predict outcomes in DoC patients. Fifty patients were enrolled within the first 10 days of hospitalization; 40 were used to train and optimize the SVM model, while 10 served as an independent test group. HRV analysis employed ratios of high-frequency and low-frequency components along with Sample Entropy to capture dynamic autonomic changes. Assessments were conducted weekly over three weeks. The SVM achieved 97% overall accuracy (misclassification 3%), 96% sensitivity, 100% specificity, and 97% balanced accuracy during training (10-fold cross-validation: 0% misclassification). In the independent test set (<i>N</i> = 10), performance was 80% overall accuracy (misclassification 20%), 80% sensitivity, 80% specificity, and 80% balanced accuracy. These results highlight the value of the HRV ratio approach, particularly the early recovery of vagal response followed by sympathetic activation, in predicting patient trajectories. Although the sample size is small, our findings support the integration of HRV analysis with machine learning as a promising tool for enhancing prognostic assessments in DoC. Future research should replicate these findings in larger cohorts and incorporate longitudinal data.</p>