Bayesian networks. A probabilistic model for chronic obstructive pulmonary disease diagnosis and phenotyping

Amos Olwendo

nov 2019 · GRIN Verlag

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Masterarbeit aus dem Jahr 2014 im Fachbereich Medizin - Sonstiges, Note: 17, , Sprache: Deutsch, Abstract: This thesis provides a model for diagnosing and classifying COPD based on phenotypes; General COPD, Chronic bronchitis, Emphysema, and the Asthmatic COPD using a Bayesian network (BN). A BN is a probabilistic modelling tool composed of random variables and the relationships of such variables is based on probabilities that maximize certain outcomes. We validated our BN model using a neural network model based on the Levenberg- Marquardt (LM) algorithm. Results show that the BN model achieved an overall classification of 98.75 % for our test cases. Furthermore, F1 score results also show that the BN is a better model for COPD classification in comparison to the LM algorithm. The World Health Organization (WHO) lists COPD as the fourth leading cause of the death worldwide yet the disease is preventable. Smoking of tobacco products, alpha-1-antitrypsin (AAt), and air pollution are the major risk factors associated with the development and progression of this disease. COPD is usually either misdiagnosed or under-diagnosed due to a number of factors including the slow progression of the development of its symptoms. Besides, differential diagnosis is usually applied during diagnosis because differentiating COPD patients from those with say chronic Asthma may not be an easy task. Previous researchers have used pulmonary function test results to diagnose COPD.