Fig 1.
Machine learning pipeline used in the study for injury detection.
The process begins with data collection, followed by data preparation. Subsequent stages involve the development and validation of predictive models to ensure accuracy and reliability in injury detection.
Fig 2.
Comparison of cost-sensitive and traditional learning approaches across 500 simulations.
This figure depicts the comparison of models’ average GMEAN values from 500 simulations, contrasting cost-sensitive learning with traditional learning methods for both train and test data splits. It also illustrates the impact of adding more features on the GMEAN scores of classifiers. The shaded areas represent the standard deviation, highlighting the variability within each model configuration.
Table 1.
Evaluation metrics of the best classifiers found.
These classifiers were chosen considering the highest geometric mean value, calculated between sensitivity and specificity on the training splits, for each type of learning and classifier.
Fig 3.
Radar plots of model performance metrics across 500 simulations.
The radar plots presented here compare the quality and stability of various models and learning types over 500 simulations, based on the best combinations of model, learning type, and feature count as identified in Table 1. Stability is assessed through standard deviations depicted as lines on top of the bars, while quality is measured in terms of GMEAN values, sensitivity (Sen), specificity (Spe), and accuracy (Acc) for both training and testing data splits.
Table 2.
Comparative analysis between results reported by the state-of-the-art works and the results attained in this work.