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Logistic regression vs machine learning to predict evacuation decisions in fire alarm situations

Abstract: In this study we assessed logistic regression and machine learning models to explore their performance in predicting evacuation decisions and to provide readers with insights into the accuracy of these methods. We tested seven machine learning algorithms, including classification and regression tree, Naïve Bayes, K-nearest neighbours, support vector machine, random forest, extreme gradient boosting, and artificial neural network. We used data collected from 1,807 participants through web-based experiments to train and calibrate these models. The performance of each model was evaluated by area under the curve, accuracy, recall, specificity, precision, and F1-score. The results indicate that logistic regression had the highest area under the curve value (0.831), whereas extreme gradient boosting outperformed other machine learning models in terms of accuracy (0.780), specificity (0.810) and precision (0.820). K-nearest neighbours model had the greater recall (0.859) and artificial neural network the highest F1-score (0.785). The models identified that being with a close person was the most influential factor in the response to a fire alarm.

 Autoría: Balboa A., Cuesta A., González-Villa J., Ortiz G., Alvear D.,

 Fuente: Safety Science, 2024, 174, 106485

 Editorial: Elsevier

 Fecha de publicación: 01/06/2024

 Nº de páginas: 10

 Tipo de publicación: Artículo de Revista

 DOI: 10.1016/j.ssci.2024.106485

 ISSN: 0925-7535,1879-1042

 Proyecto español: PID2019-106025RB-100

 Url de la publicación: https://doi.org/10.1016/j.ssci.2024.106485