Machine Learning in Failure Prediction in Breathalyzers

To reduce accidents and deaths on roads, several countries have established laws that consider driving under the influence as a traffic violation [1, 2]. In Brazil, the instruments have to be in accordance with the OIML (R126) [3]. Errors in readings can result in unfair punishment if shows values above or below the real concentration. This study focused on identifying the probability of instrument failure before it ocurrs. This study addresses the use of classification models to predict failures that may occur between periodic checks performed every 12 months. The results showed the extra tree classifier model exhibited the best result, with an accuracy and precision of 75 and 77%, respectively. Machine learning models in metrological control is an alternative to add measurement reliability, providing legal certainty for the imposed infractions, and more accurately identifying drivers under the influence of alcohol and removing them from the roads.