An Effective Random Generalised Linear Model to Predict COPD

Conference paper


Saraireh, L., Sharif, S. and Alsallal, M. 2022. An Effective Random Generalised Linear Model to Predict COPD. 3ICT 2022: International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies. University of Bahrain, Bahrain 20 - 21 Nov 2022 IEEE. https://doi.org/10.1109/3ICT56508.2022.9990712
AuthorsSaraireh, L., Sharif, S. and Alsallal, M.
TypeConference paper
Abstract

Chronic obstructive pulmonary disease (COPD) is a type of chronic lung illness that worsens with time and leads to a restriction in the outflow of air from the lungs. According to the World Health Organisation, The World Health Organization ranks COPD as the third leading cause of death. Clinically, the diagnosis of this disease is relatively difficult; therefore, early identification of individuals at risk of developing COPD is vital for implementing preventative strategies. This research work has developed a generalised linear model (GLM) to predict the COPD status of the patients. A dataset of 1262 patients (688 COPD cases and 574 controls) was used. Exploratory data analysis (EDA) was utilised to observe how potential covariates were related to the response variable (COPD status). By employing rigorous model selection techniques (forward selection and backwards elimination) according to (AIC) which stand from Akaike information criterion and (BIC) which stand from Bayesian information criterion (BIC), a consensus was reached that the most suitable model is a binomial logistic regression model which includes the smoking history, gender, and age. The model was validated using an independent test set with an accuracy of 73%. Such a model, once fully validated, has the ability for predicting the risk of developing COPD in patients with existing lung conditions, including but not limited to, asthma.

Year2022
Conference3ICT 2022: International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies
PublisherIEEE
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Publication dates
Online30 Dec 2022
Publication process dates
Accepted09 Sep 2022
Deposited12 Sep 2022
Journal citationpp. 227-232
ISSN2770-7466
Book title2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)
ISBN9781665451932
9781665451949
Digital Object Identifier (DOI)https://doi.org/10.1109/3ICT56508.2022.9990712
Web address (URL) of conference proceedingshttps://ieeexplore.ieee.org/xpl/conhome/9989532/proceeding
Copyright holder© 2022, IEEE
Copyright informationPersonal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Sharif, M., Qahwaji, Rami, Hayajneh, Sofyan, Ipson, Stanley, Alzubaidi, Rania and Brahma, Arun 2014. An efficient system for preprocessing confocal corneal images for subsequent analysis. in: 2014 14th UK Workshop on Computational Intelligence (UKCI) IEEE.
Artificial Neural Network-Based System for PET Volume Segmentation
Sharif, M., Abbod, Maysam, Amira, Abbes and Zaidi, Habib 2010. Artificial Neural Network-Based System for PET Volume Segmentation. International Journal of Biomedical Imaging. 2010 (105610). https://doi.org/10.1155/2010/105610
Artificial Neural Network-Statistical Approach for PET Volume Analysis and Classification
Sharif, M., Abbod, Maysam, Amira, Abbes and Zaidi, Habib 2012. Artificial Neural Network-Statistical Approach for PET Volume Analysis and Classification. Advances in Fuzzy Systems. 2012 (327861). https://doi.org/10.1155/2012/327861
Machine Learning Optimisation for Realistic 2D and 3D PET-CT Phantom Study
Sharif, M., Abbod, Maysam, Sonoda, Luke I. and Sanghera, Bal 2013. Machine Learning Optimisation for Realistic 2D and 3D PET-CT Phantom Study. British Journal of Applied Science & Technology. 4 (4), pp. 634-649. https://doi.org/10.9734/bjast/2014/5084
Preparation of 2D sequences of corneal images for 3D model building
Elbita, Abdulhakim, Qahwaji, Rami, Ipson, Stanley, Sharif, M. and Ghanchi, Faruque 2015. Preparation of 2D sequences of corneal images for 3D model building. Computer Methods and Programs in Biomedicine. 114 (2), pp. 194-205. https://doi.org/10.1016/j.cmpb.2014.01.009
Medical image classification based on artificial intelligence approaches: A practical study on normal and abnormal confocal corneal images
Sharif, M., Qahwaji, R., Ipson, S. and Brahma, A. 2015. Medical image classification based on artificial intelligence approaches: A practical study on normal and abnormal confocal corneal images. Applied Soft Computing. 36 (Nov.), pp. 269-282. https://doi.org/10.1016/j.asoc.2015.07.019
An efficient intelligent analysis system for confocal corneal endothelium images
Sharif, M., Qahwaji, R., Shahamatnia, E., Alzubaidi, R., Ipson, S. and Brahma, A. 2015. An efficient intelligent analysis system for confocal corneal endothelium images. Computer Methods and Programs in Biomedicine. 122 (3), pp. 421-436. https://doi.org/10.1016/j.cmpb.2015.09.003
In Vivo Confocal Microscopic Corneal Images in health and disease with an emphasis on extracting features and visual signatures for corneal diseases: A review study
Alzubaidi, Rania, Sharif, M., Qahwaji, Rami, Ipson, Stanley and Brahma, Arun 2015. In Vivo Confocal Microscopic Corneal Images in health and disease with an emphasis on extracting features and visual signatures for corneal diseases: A review study. British Journal of Ophthalmology. 100 (1), pp. 41-55. https://doi.org/10.1136/bjophthalmol-2015-306934
A Mutlimodal Approach to Measure the Levels Distraction of Pedestrians using Mobile Sensing
Pizzamiglio, S., Naeem, U., ur Réhman, Shafiq, Sharif, M., Abdalla, H. and Turner, D. 2017. A Mutlimodal Approach to Measure the Levels Distraction of Pedestrians using Mobile Sensing. Procedia Computer Science. 113, pp. 89-96. https://doi.org/10.1016/j.procs.2017.08.297