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

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Published: Jun 29, 2020
Keywords:
Chronic Obstructive Pulmonary Disease, Chronic Bronchitis, Emphysema, Machine Learning, Bayesian Belief Network, Diagnosis

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Alile Solomon.O
Bello Moses.E

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

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How to Cite
[1]
Alile Solomon.O and Bello Moses.E, “Diagnosing Chronic Obstructive Pulmonary Disease (COPD) Using Bayesian Belief Network”, Int. J. Comput. Eng. Res. Trends, vol. 7, no. 6, pp. 1–12, Jun. 2020.
Issue
Vol. 7 No. 6 (2020): June (2020) Issue
Section
Research Articles
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