Integrating Human Factors and Predictive Modeling for Enhanced Aviation Safety: A Data-Driven Approach to Risk Mitigation and System Design

The evolution of the aviation industry from manual control to highly automated and autonomous systems has brought human factors to the forefront of safety, design, and operational efficiency. This paper presents an integrated analysis combining machine learning techniques with statistical modeling to assess the impact of human-system interaction on aviation incident outcomes. The first phase of this research involved a longitudinal dataset covering aircraft design evolution, training programs, and incident records. Using classification algorithms, including XGBoost, the study achieved an accuracy of 81.82% in predicting incident likelihood. Performance metrics such as F1-scores (0.85 for “Incident” and 0.78 for “No Incident”) highlighted the model’s robustness in identifying risk patterns associated with human factors and system complexity. In the second phase, 50 plus detailed aviation incident reports were analyzed using logistic regression, t-tests, and Cook’s Distance to identify high-influence predictors. Human-related issues—particularly training deficiencies, poor communication, and situational awareness failures—were statistically significant predictors of crash outcomes (p = 0.0016), whereas engineering and organizational variables showed limited correlation. The combined analysis reveals that failures in human-system interaction significantly outweigh technical failures in contributing to adverse outcomes. Based on these findings, the paper proposes actionable design principles, including adaptive training platforms, real-time communication monitoring tools, predictive cockpit alerts, and lifecycle integration of Human Factors and Risk Assessment processes. This research offers a comprehensive framework for data-driven safety enhancement in aviation, merging predictive analytics with sociotechnical design principles to reduce latent vulnerabilities and improve overall system resilience.