| Sumario: | Purpose: Optimal performance in military tasks is crucial for operational success. These tasks are often simulated in training, assessing personnel performance within a military environment. However, these assessments are time-consuming and a potential injury risk. Physical characteristics such as muscular strength, power, aerobic endurance, and circumferences can be used to predict these dynamic and demanding tasks. Utilizing machine learning models to predict assessment outcomes may lead to optimized management of personnel, time, and interventions in the military. Methods: This study recruited 35 participants to complete two physical sessions assessing multiple physical characteristics and lift-to-place and jerry-can-carry assessments. Machine learning models were developed to predict assessment outcomes based on a down-selection of physical characteristics metrics. Root mean square error (RMSE), normalized root mean square error (NRMSE), and coefficient of variation of the root mean square error (CVRMSE) were used to evaluate the models' predictive capabilities. Results: The support vector regression (SVR) and ridge models could predict the lift-to-place outcome to an RMSE of ±1.77 kg (NRMSE = 4.44%, CVRMSE = 0.18) and ±2.33 kg (NRMSE = 5.84%; CVRMSE = 0.24) with four and three physical tests, respectively. The multilayer perceptron and SVR models predicted the jerry-can-carry outcome to ±3.36 laps (NRMSE = 23.06%, CVRMSE = 0.39) and ±3.67 laps (NRMSE = 25.20%, CVRMSE = 0.42) with 12 and 8 physical tests, respectively. Conclusions: The lift-to-place outcome can be accurately predicted, showing potential military implementation. The jerry-can-carry outcome shows promise; however, further model optimization and training metrics are required to reduce error. Machine learning models demonstrate their applicability to optimize occupational selection pathways and training interventions for desirable performance in military settings.
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