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    Machine learning, specifically Random Forests, identified key factors influencing helicopter pilot performance. Higher job rewards and predictability improved scores, while physiological dysregulation and liver enzymes indicated lower performance, enhancing flight safety analysis.

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    Area of Science:

    • Aviation Safety
    • Human Factors
    • Machine Learning

    Background:

    • Aviation safety is influenced by complex interactions within the system, including pilot fitness, supervision, and working conditions.
    • Identifying potential flight safety hazards from numerous variables requires advanced analytical methods.
    • Machine learning, such as Random Forests, offers a promising approach to analyze these complex interactions.

    Purpose of the Study:

    • To apply machine learning to identify significant predictors of simulator flight performance in helicopter emergency medical services pilots.
    • To screen a large set of pilot-related and occupational factors for their impact on flight safety.
    • To explore the relationship between specific physiological and psychological factors and pilot performance.

    Main Methods:

    • Utilized the Random Forest machine learning method for a cross-sectional, explorative analysis.
    • Analyzed 54 candidate predictors from self-report questionnaires and aeromedical records of 51 male pilots.
    • Selected informative predictors based on the conditional permutation variable importance (VI) statistic.

    Main Results:

    • Five predictors exceeded the selection threshold: higher perceived rewards and predictability were linked to better performance.
    • Higher physiological dysregulation and alanine aminotransferase levels were associated with lower performance scores.
    • Significant performance differences were observed between simulators at two training sites.

    Conclusions:

    • Random Forests can effectively complement existing methods for identifying human factors in flight safety.
    • The identified predictors highlight specific areas for potential safety improvements in aviation.
    • Understanding these factors can lead to targeted interventions to enhance pilot performance and overall flight safety.