Enhancing aviation safety: An 80-year data-driven model for classification of aviation incident and accident
Clinical Snapshot
PICO Framework
| P — Population | 53,770 aviation occurrence summaries from Transportation Safety Board of Canada spanning 80 years |
| I — Intervention | Machine learning classifiers (Multinomial Naive Bayes, Random Forest, Support Vector Machine) with TF-IDF vectorization |
| C — Comparator | Comparison between three different machine learning algorithms |
| O — Outcomes | Classification accuracy, precision, recall for distinguishing aviation incidents from accidents |
Bottom Line
This machine learning study demonstrates that automated classification of aviation incidents versus accidents can achieve 98.06% accuracy using Support Vector Machine algorithms on textual safety reports. The research addresses a practical operational need in aviation safety management, where manual review of thousands of reports creates bottlenecks and inconsistencies. The 80-year Canadian dataset provides robust validation, though external validation across different aviation authorities would strengthen generalizability. For Australian aviation medicine and safety professionals, this technology could significantly enhance CASA's safety oversight capabilities and airline safety management systems. The high accuracy suggests potential for rapid triage of safety reports, early identification of serious events, and detection of potential mislabeling. However, the safety-critical nature of aviation requires careful consideration of false negative rates, as misclassifying a serious accident as an incident could have significant consequences. Implementation should include human oversight and regular algorithm performance monitoring.
Key Findings
P Value: Not reported
Effect Size: 98.06% accuracy with Support Vector Machine classifier
Primary Outcome: Classification accuracy for distinguishing aviation incidents from accidents
Nnt Or Sensitivity: Sensitivity and specificity values not provided in abstract
Confidence Interval: Not explicitly reported, but consistent across 5-fold cross-validation
Clinical Application
High feasibility for implementation in automated safety report processing systems Directly applicable to Civil Aviation Safety Authority (CASA) operations and Australian airline safety management systems under ICAO standards Aviation safety management systems, regulatory authorities, airline operators
Abstract
The aviation system is safety-critical by nature, and any occurrence of an incident or accident can lead to the loss of human life and significant operational disruptions. The International Civil Aviation Organization (ICAO) emphasizes that every flight must take off and land safely-a goal achieved over 126,000 times daily. Despite major advancements,mishaps and accidents continue to occur, underscoring the need for robust safety management systems. The accurate classification of aviation occurrences (Incident or Accident) reports is essential for safety management, yet manual review is time-consuming and prone to inconsistency. While incident/accident labels are assigned during reporting, automated classification enables rapid triage, detection of potential mislabeling, and support for severity assessment in high-volume aviation safety operations. To address this,we developed and compared three machine learning classifiers-Multinomial Naive Bayes, Random Forest, and Support Vector Machine-using TF-IDF vectorization on an 80-year dataset of 53,770 aviation occurrence summaries obtained from the Transportation Safety Board of Canada. A two-stage evaluation strategy was employed, consisting of an initial 80/20 train-test split to create an independent test set, followed by 5-fold cross-validation applied exclusively to the training data to ensure robustness and prevent optimistic bias. The Support Vector Machine (SVM) classifier achieved the highest classification performance, attaining an accuracy of 98.06% during 5-fold cross-validation, with consistent results across folds, demonstrating its effectiveness in managing high-dimensional textual data and dataset complexity. The proposed framework provides a robust foundation for automated aviation safety report processing, offering practical value for (1) early triage of safety reports, (2) identification of potentially mislabeled cases requiring expert review, and (3) integration into downstream severity assessment pipelines. This work advances beyond prior classification studies by establishing a benchmark on the largest historical aviation safety dataset while delivering a deployable and operationally relevant framework for real-world safety management applications. The findings offer valuable insights for regulatory authorities and airline operators, contributing to enhanced safety oversight, improved response strategies, and safer aviation operations.
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