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Mathematical regression analysis of oxygen saturation for driving fatigue using Box-Behnken design

Like many other countries, the number of fatigue-related traffic accidents in Malaysia have become an alarming concern. Mathematical modelling is the process of describing a real-world issue in mathematical terms to understand the original issue. Hence, this paper aims to develop a mathematical regr...

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Main Authors: Ibrahim, Muhammad Shafiq, Kamat, Seri Rahayu, Shamsuddin, Syamimi, Fukumi, Minoru
Format: Article
Language:English
Published: IJETAE Publication House 2022
Online Access:http://eprints.utem.edu.my/id/eprint/26448/3/IJETAE_0922_03.pdf
http://eprints.utem.edu.my/id/eprint/26448/
https://www.ijetae.com/files/Volume12Issue9/IJETAE_0922_03.pdf
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spelling my.utem.eprints.264482023-03-28T13:56:09Z http://eprints.utem.edu.my/id/eprint/26448/ Mathematical regression analysis of oxygen saturation for driving fatigue using Box-Behnken design Ibrahim, Muhammad Shafiq Kamat, Seri Rahayu Shamsuddin, Syamimi Fukumi, Minoru Like many other countries, the number of fatigue-related traffic accidents in Malaysia have become an alarming concern. Mathematical modelling is the process of describing a real-world issue in mathematical terms to understand the original issue. Hence, this paper aims to develop a mathematical regression model to predict the relationship between five input variables namely (i) driving duration, (ii) driving speed, (iii) body mass index (BMI), (iv) types of roads and (v) gender and an output response (oxygen saturation level) as the causes of driving fatigue. The regression analysis utilized Box-Behnken design method by Design Expert (6.0.8) software. The results revealed that the Prob > F values for all input variables were less than 0.01%, implying that all the variables were significant in influencing the oxygen saturation level. The regression model was validated to determine its accuracy in predicting the output response. The analysis presented excellent prediction accuracy as the model was capable to predict the data within 95% predictive interval, which met the minimum quantitative condition of 90% predictive interval. Furthermore, the residual errors were less than 10%, indicating that the model has excellent accuracy in predicting the oxygen saturation. The model prediction is expected to be useful in guiding researchers and policy makers in road safety field to take measures in minimizing traffic accidents due to driving fatigue. IJETAE Publication House 2022-09 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/26448/3/IJETAE_0922_03.pdf Ibrahim, Muhammad Shafiq and Kamat, Seri Rahayu and Shamsuddin, Syamimi and Fukumi, Minoru (2022) Mathematical regression analysis of oxygen saturation for driving fatigue using Box-Behnken design. International Journal of Emerging Technology and Advanced Engineering, 12 (9). 23 - 29. ISSN 2250-2459 https://www.ijetae.com/files/Volume12Issue9/IJETAE_0922_03.pdf 10.46338/ijetae0922_03
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
description Like many other countries, the number of fatigue-related traffic accidents in Malaysia have become an alarming concern. Mathematical modelling is the process of describing a real-world issue in mathematical terms to understand the original issue. Hence, this paper aims to develop a mathematical regression model to predict the relationship between five input variables namely (i) driving duration, (ii) driving speed, (iii) body mass index (BMI), (iv) types of roads and (v) gender and an output response (oxygen saturation level) as the causes of driving fatigue. The regression analysis utilized Box-Behnken design method by Design Expert (6.0.8) software. The results revealed that the Prob > F values for all input variables were less than 0.01%, implying that all the variables were significant in influencing the oxygen saturation level. The regression model was validated to determine its accuracy in predicting the output response. The analysis presented excellent prediction accuracy as the model was capable to predict the data within 95% predictive interval, which met the minimum quantitative condition of 90% predictive interval. Furthermore, the residual errors were less than 10%, indicating that the model has excellent accuracy in predicting the oxygen saturation. The model prediction is expected to be useful in guiding researchers and policy makers in road safety field to take measures in minimizing traffic accidents due to driving fatigue.
format Article
author Ibrahim, Muhammad Shafiq
Kamat, Seri Rahayu
Shamsuddin, Syamimi
Fukumi, Minoru
spellingShingle Ibrahim, Muhammad Shafiq
Kamat, Seri Rahayu
Shamsuddin, Syamimi
Fukumi, Minoru
Mathematical regression analysis of oxygen saturation for driving fatigue using Box-Behnken design
author_facet Ibrahim, Muhammad Shafiq
Kamat, Seri Rahayu
Shamsuddin, Syamimi
Fukumi, Minoru
author_sort Ibrahim, Muhammad Shafiq
title Mathematical regression analysis of oxygen saturation for driving fatigue using Box-Behnken design
title_short Mathematical regression analysis of oxygen saturation for driving fatigue using Box-Behnken design
title_full Mathematical regression analysis of oxygen saturation for driving fatigue using Box-Behnken design
title_fullStr Mathematical regression analysis of oxygen saturation for driving fatigue using Box-Behnken design
title_full_unstemmed Mathematical regression analysis of oxygen saturation for driving fatigue using Box-Behnken design
title_sort mathematical regression analysis of oxygen saturation for driving fatigue using box-behnken design
publisher IJETAE Publication House
publishDate 2022
url http://eprints.utem.edu.my/id/eprint/26448/3/IJETAE_0922_03.pdf
http://eprints.utem.edu.my/id/eprint/26448/
https://www.ijetae.com/files/Volume12Issue9/IJETAE_0922_03.pdf
_version_ 1761623120342417408
score 13.211869

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