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One of the main challenges in traumatic brain injury (TBI) patients is to achieve an early and definite prognosis. Despite the recent development of algorithms based on artificial intelligence for the identification of these prognostic factors relevant for clinical practice, the literature lacks a rigorous comparison among classical regression and machine learning (ML) models. The utility of comparing traditional regression modeling to ML is highlighted here, particularly when using a small number of reliable predictor variables after TBI. The dataset of clinical data used to train ML algorithms will be publicly available to other researchers for future comparisons.
Classification Model | |||||||
---|---|---|---|---|---|---|---|
Cross Validation | Performance Metric | Linear Model | Support Vector Machine (SVM) | k-Nearest Neighbors (k-NN) |
Naïve Bayes (NB) | Decision Tree (DT) | Ensemble |
LOOCV | Accuracy | 84.69% | 80.61% | 82.65% | 64.29% | 79.59% | 83.67% |
Precision µ | 84.69% | 80.61% | 82.65% | 64.29% | 79.59% | 83.67% | |
Precision M | 83.46% | 79.51% | 81.20% | 64.29% | 78.14% | 82.47% | |
Recall µ | 64.84% | 58.09% | 61.36% | 37.50% | 56.52% | 63.08% | |
Recall M | 41.51% | 38.65% | 40.40% | 25.00% | 38.25% | 40.79% | |
F1 Score µ | 73.45% | 67.52% | 70.43% | 47.37% | 66.10% | 71.93% | |
F1 Score M | 55.44% | 52.02% | 53.95% | 36.00% | 51.36% | 54.59% | |
10-fold CV | Accuracy | 85.89% | 81.78% | 82.78% | 64.33% | 76.78% | 81.78% |
Precision µ | 85.71% | 81.63% | 82.65% | 64.29% | 76.53% | 81.63% | |
Precision M | 84.44% | 80.50% | 81.20% | 64.29% | 74.53% | 80.00% | |
Recall µ | 66.67% | 59.70% | 61.36% | 37.50% | 52.08% | 59.70% | |
Recall M | 42.22% | 39.37% | 40.40% | 25.00% | 36.75% | 40.00% | |
F1 Score µ | 75.00% | 68.97% | 70.43% | 47.37% | 61.98% | 68.97% | |
F1 Score M | 56.30% | 52.87% | 53.95% | 36.00% | 49.22% | 53.33% |
Classification Model | |||||||
---|---|---|---|---|---|---|---|
Cross Validation |
Performance Metric | Linear Model | Support Vector Machine (SVM) | k-Nearest Neighbors (k-NN) |
Naïve Bayes (NB) | Decision Tree (DT) | Ensemble |
LOOCV | Accuracy | 65.31% | 43.88% | 66.33% | 68.37% | 59.18% | 71.43% |
Precision µ | 65.31% | 43.88% | 66.33% | 68.37% | 59.18% | 71.43% | |
Precision M | 66.49% | 36.49% | 66.50% | 52.94% | 46.16% | 71.73% | |
Recall µ | 38.55% | 20.67% | 39.63% | 41.88% | 32.58% | 45.45% | |
Recall M | 36.51% | 23.14% | 37.28% | 38.74% | 30.86% | 41.58% | |
F1 Score µ | 48.48% | 28.10% | 49.62% | 51.94% | 42.03% | 55.56% | |
F1 Score M | 47.13% | 28.32% | 47.77% | 44.74% | 36.99% | 52.64% | |
10-fold CV | Accuracy | 64.22% | 45.00% | 65.33% | 67.44% | 57.44% | 70.44% |
Precision µ | 64.28% | 44.89% | 65.31% | 67.34% | 57.14% | 70.41% | |
Precision M | 65.72% | 36.83% | 65.44% | 52.16% | 45.83% | 70.78% | |
Recall µ | 37.50% | 21.36% | 38.55% | 40.74% | 30.77% | 44.23% | |
Recall M | 35.73% | 23.42% | 36.38% | 37.78% | 28.68% | 40.66% | |
F1 Score µ | 47.37% | 28.95% | 48.48% | 50.77% | 40.00% | 54.33% | |
F1 Score M | 46.29% | 28.63% | 46.77% | 43.82% | 35.28% | 51.65% |