Understanding the Incidents on Legacy Airlines with Explainable Artificial Intelligence and Text Mining: Case Study Top 5 US Airlines

Abstract

Aircraft incident data from the Federal Aviation Administration between 1978-2023 was analyzed for the top five airlines in the United States: Delta Air Lines Inc., American Airlines Inc., Alaska Airlines Inc., Southwest Airlines Co., and United Airlines Inc. This analysis aimed to predict conditions with a higher chance of causing injury and determine the best machine-learning model for this prediction. The target variable of injuries occurred is processed through machine learning methods such as Naïve Bayes, logistic regression, and XGBoost. This was to predict the injuries and understand and explain the complex fault mechanism with explainable artificial intelligence. The XGBoost model gave the best predictive performance at 93% receiver operating characteristic, 84.4% precision, and 88.2% recall. This provided insights into improving safety in the aviation industry. It contributed to the literature by predicting injuries in legacy airlines, explaining the patterns that create incidents, and creating prescriptions to reduce injuries that happen on airlines using explainable machine learning and text-mining models