Table 2.

One–R classifier results and confusion matrix.

Confusion Matrix	Classifier: One-R
	Rule: CIl < 4.876 → UWS CIl ≥ 4.876 → MCS
	Test dataset results
	Full training test (S1 group)	10-fold cross-validation	fMRI test (S2 subgroup)
True positive (MCS) rate (%)	94	88	92
True negative (UWS) rate (%)	93	93	100
False negative rate (%)	6	13	8
False positive rate (%)	7	7	0
Precision MCS classification (%)	94	94	100
Precision UWS classification (%)	93	87	91
accuracy (%)	93	90	95
F1-score (%)	94	90	96
Matthews Correlation Coefficient[−1:1]	0.87	0.80	0.91

The confusion matrix is based on the S1 group. The One-R classifier is a simple machine learning decision tree model that derives a single rule from the single most contributing parameter to predict the patient's diagnosis. This model deduced that the long term complexity index (CI_l) is the best predictor of patient's diagnosis, with a threshold of ~4.9, below which the patient should be diagnosed as unresponsive (UWS) and above as minimally conscious (MCS). The 10-fold cross-validation test shows that this model is quite robust and reliable, with 90% accuracy, 7% false positive rate, 13% false negative rate and a F1-score, combining both accuracy and recall, of 90%. For comparison, a baseline Zero-R rule always predicting MCS as the diagnosis would have an accuracy of 53% on the S1 group dataset. Additional machine learning models and results can be found in the Supplementary materials (Appendix A).