Impact Factor:6.549
 Scopus Suggested Journal: Tracking ID for this title suggestion is: 55EC484EE39417F0

International Journal
of Computer Engineering in Research Trends (IJCERT)

Scholarly, Peer-Reviewed, Platinum Open Access and Multidisciplinary




Welcome to IJCERT

International Journal of Computer Engineering in Research Trends. Scholarly, Peer-Reviewed, Platinum Open Access and Multidisciplinary

ISSN(Online):2349-7084                 Submit Paper    Check Paper Status    Conference Proposal

Back to Current Issues

Diagnosing Chronic Obstructive Pulmonary Disease (COPD) Using Bayesian Belief Network

Alile Solomon.O, Bello Moses.E, , ,
Affiliations
1 &2 : Computer Science, Faculty of Physical Sciences, University of Benin, Benin City, Nigeria
:10.22362/ijcert/2020/v7/i06/v7i0601


Abstract
Chronic Obstructive Pulmonary Disease (COPD) is a death-defying respiratory tract ailment that causes trouble in breathing which deteriorates after some time. COPD is an umbrella term used to order the amalgamation of Chronic Bronchitis and Emphysema. The manifestations of this infection are frequent coughing, fatigue, sweating, breathlessness, tiredness, weight loss, wheezing, fast heart rate, fast breathing and chest tightness just to name not many. This malady is pervasive with individuals whose age ranges from 30 or more and afterward arrives at its top in patients over 50. Because of the covering manifestations this malady imparts to other respiratory tract illnesses; it is in some cases under-analyzed and misdiagnosed a circumstance which is much uncontrolled in Sub-Sahara Africa. In time past, COPD has caused a large number of deaths overall yearly because of absence of early determination of the illness. In ongoing past, a few frameworks have been created to analyze this non-transmittable malady, yet they produced a ton of bogus negative during testing and couldn't identify COPD because of its covering side effects it imparts to other respiratory tract illnesses. Consequently, in this paper, we proposed and developed a model to foresee COPD utilizing an AI procedure called Bayesian Belief Network. The model was structured utilizing Bayes Server and tested with data gathered from COPD medical repository. The model had a general expectation precision of 99.98%; 99.79%, 95.91% and 98.39% sensitivity of COPD, Chronic Bronchitis and Emphysema in that order.


Citation
Alile Solomon.O,Bello Moses.E."Diagnosing Chronic Obstructive Pulmonary Disease (COPD) Using Bayesian Belief Network". International Journal of Computer Engineering In Research Trends (IJCERT) ,ISSN:2349-7084 ,Vol.7, Issue 06,pp.1-12, June - 2020, URL :https://ijcert.org/ems/ijcert_papers/V7I601.pdf,


Keywords : Chronic Obstructive Pulmonary Disease, Chronic Bronchitis, Emphysema, Machine Learning, Bayesian Belief Network, Diagnosis.

References
[1] M.A. Zoroddu, J. Aashet, G. Crisponi, S. Medici, M. Peana and V.M. Nurchi, “The Essentials Metals for Humans: A Brief Overview”. Journal of Inorganic Biochemistry. 195:.DOI:10.1016/j.jinorgbio. 2019. pp.120-129.
[2] GOLD Executive Committee, “Global Initiative for Chronic Obstructive Lung Disease (GOLD): Global Strategy for the Diagnosis, Management and Prevention of Chronic Obstructive Pulmonary Disease”. 2006 MCR Vision. Inc. pp. 1-15.
[3] American Thoracic Society A. (2016): “Chronic Obstructive Pulmonary Disease (COPD)”. American Journal of Respiratory and Critical Care Medicine Vol.199 (1), pp. 1-2, 2019.
[4] M. Varmaghani, M. Dehghani, E. Heidari, F. Sharifi, S.S. Moghaddam and F.Farzadfar,“Global Prevelence of Chronic Obstructive Pulmonary Disease: Systematic Review and Meta-Analysis”. Eastern Mediterranean Health Journal, Vol.25 (1), 2019. 
[5] World Health Organization, “Global Status Report Status Report on Non-Communicable Diseases 2010: Description of the Global Burden of NCDs, Their Risk Factors and Determinants”, April, 2011, ISBN: 978-92-4-156422-9, pp. 1-176.
[6] S. Salvi, (2017): “The Silent Epidemic of COPD in Africa”. Lancet Global Health, Doi: 10.1016/S2214-109X(14)70359-6, 2015; 3(1), pp. e6-e7.
[7] FIRS, “Respiratory Diseases in the World: Realities Today-Opportunities for Tomorrow”. 2013, ISBN: 978-1-84984-057-6, pp. 1-35.
[8] D.A. Grant, P.J. Van Harrison, R., Othman, A., Roark, S.E., Han, M.K., Remington, T.L. (2017): “Chronic Obstructive Pulmonary Disease”. University of Michigan Hospitals and Health Centres, COPD Guideline, November 2017, pp. 1-28.
[9] J.C. Hogg, F. Chu, , S. Utokaparch, W.M. Elliot, , L. Buzatu, , R.M. Cherniack, F.C. Sciurba, H.O, Coxson and P.D, Pare,  “The Nature of Small Airway Obstruction in Chronic Obstructive Pulmonary Disease”. New England Journal of Medicine 2004; Vol. 350, pp. 2645–2653.
[10] M. Zolnoori,  M.H.F. Zarandi and M. Moin,  "Fuzzy Rule-Base Expert System for Evaluation Possibility of Fatal Asthma". Journal of Health Informatics in Developing Countries, 2010, pp: 171-184.
[11] I.C. Mary and J. Preethi, "A Survey on Computerized Detection, Quantification and Classification of Lung Disease". IJAICT Vol. 1(7), November 2014, Doi:01.0401/ijaict.2014.07.25, ISSN 2348–9928. pp. 631-634.
[12] D. Sanchez-Morillo, M.A. Fernandez-Granero and A. Leon-Jimenez, “Use of Predictive Algorithms in-Home Monitoring of Chronic Obstructive Pulmonary Disease and Asthma: A Systematic Review”. Chronic Respiratory Disease 2016, Vol. 13(3), pp. 264–283. DOI: 10.1177/1479972316642365.
[13] G. Gonzalez, S.Y. Ash, , G. Vegas-Sanchez-Ferrero, J.O. Onieva, F.N. Rahaghi, J.C. Ross, A. D?az, R.S.J. Estepar and G.R. Washko, “Disease Staging and Prognosis in Smokers Using Deep Learning in Chest Computed Tomography”. American Journal of Respiratory Critical Care Medicine, Vol. 197(2), Jan 15, 2018, pp. 193–203. DOI: 10.1164/rccm.201705-0860OC.
[14] P. ShubhaDeepti, S.V.N.N. Rao, V.N. Kumar and Y.P. Sai, “Expert System using Artificial Neural Network for Chronic Respiratory Diseases”. ISSN (PRINT): 2393-8374, (ONLINE): 2394-0697, 2017, Vol.4 (9), pp. 6-14.
[15] A. Badnjevic, L. Gurbeta and E. Custovic, “An Expert Diagnostic System to Automatically Identify Asthma and Chronic Obstructive Pulmonary Disease in Clinical Settings”. 8:11645 | DOI:10.1038/s41598-018-30116-2. 2018, pp. 1-9.
[16] F. Braido, P. Santus, A.G. Corsico, F. Di Marco, G. Melioli, , N. Scichilone and P. Solidoro,  “Chronic Obstructive Lung Disease “Expert System”: Validation of A Predictive Tool for Assisting Diagnosis”. International Journal of COPD, Vol.2018 (13), pp. 1747–1753.
[17] R.H. Abiyev and M.K.S. Ma’aitah, “Deep Convolutional Neural Networks for Chest Diseases Detection”. Hindawi Journal of Healthcare Engineering Vol. 2018(4168538), pp.1-11. 
[18] S. Sathiya, G.Priyanka and S. Jeyanthi, “Detection of Chronic Obstructive Pulmonary Disease in Computer Aided Diagnosis System with CNN Classification”. International Journal of Pure and Applied Mathematics Vol. 119(12), 2018, pp. 13815-13822, Special Issue, ISSN: 1314-3395. 
[19] I. Ben-Gal, F. Ruggeri, F. Faltin, R. Kenett, “Bayesian Networks”. Encyclopedia of Statistics in Quality and Reliability. John Wiley and Sons, Ltd, 2007, pp. 1-6.
 [20] V. Cheplygina, I.P. Peña, J.H. Pedersen, D.A. Lynch, L. Sørensen and M. De Bruijne, “Transfer Learning for Multi-Center Classification of Chronic Obstructive Pulmonary Disease” International Journal of Biomedical and Health Informatics Vol. 22(5), 2018 pp. 1486-1496. DOI 10.1109/JBHI.2017.2769800.


DOI Link : https://doi.org/10.22362/ijcert/2020/v7/i06/v7i0601

Download :
  V7I601.pdf


Refbacks : Currently there are no Refbacks

Support Us


We have kept IJCERT is a free peer-reviewed scientific journal to endorse conservation. We have not put up a paywall to readers, and we do not charge for publishing. But running a monthly journal costs is a lot. While we do have some associates, we still need support to keep the journal flourishing. If our readers help fund it, our future will be more secure.

Quick Links



DOI:10.22362/ijcert


Science Central

Score: 13.30





Submit your paper to editorijcert@gmail.com