Open AccessImpact of Work Schedules on the Sleep Patterns of Railroad Workers using CHAID Neural Network and Ensemble Models of Machine Learning
Author(s) -
Divya Gupta,
Neerja Pande,
Jitendra Shreemali,
Prąsun Chakrabarti
Publication year - 2020
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.c1004.0393s20
Subject(s) - chaid , sleep (system call) , artificial neural network , work (physics) , sleep disorder , sleep apnea , obstructive sleep apnea , computer science , psychology , machine learning , medicine , engineering , psychiatry , insomnia , decision tree , mechanical engineering , cardiology , operating system
The study examines the impact of work schedules on the sleep patterns of railroad workers in the USA. The study used the CHAID model, Neural Network and the Ensemble model to identify factors that have a greater impact on sleep patterns. Age, number of children / dependents are found to be key factors for sleep apnea as well as sleep disorders while job pressure and work hours are seen to be the third factor for sleep apnea and sleep disorder respectively. CHAID model provided the highest accuracy (92%) for sleep disorder while the ensemble model provided an accuracy of over 93% for sleep apnea.