Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/161813
Authors: 
Emanet, Nahit
Öz, Halil R.
Bayram, Nazan
Delen, Dursun
Year of Publication: 
2014
Citation: 
[Journal:] Decision Analytics [ISSN:] 2193-8636 [Volume:] 1 [Year:] 2014 [Issue:] 1 [Pages:] 1-20
Abstract: 
Advanced analytical techniques are gaining popularity in addressing complex classification type decision problems in many fields including healthcare and medicine. In this exemplary study, using digitized signal data, we developed predictive models employing three machine learning methods to diagnose an asthma patient based solely on the sounds acquired from the chest of the patient in a clinical laboratory. Although, the performances varied slightly, ensemble models (i.e., Random Forest and AdaBoost combined with Random Forest) achieved about 90% accuracy on predicting asthma patients, compared to artificial neural networks models that achieved about 80% predictive accuracy. Our results show that non-invasive, computerized lung sound analysis that rely on low-cost microphones and an embedded real-time microprocessor system would help physicians to make faster and better diagnostic decisions, especially in situations where x-ray and CT-scans are not reachable or not available. This study is a testament to the improving capabilities of analytic techniques in support of better decision making, especially in situations constraint by limited resources.
Subjects: 
Classification
Data mining
Machine learning
Decision making
Asthma
Pulmonary sound signals
Discrete wavelet transformation
Persistent Identifier of the first edition: 
Creative Commons License: 
http://creativecommons.org/licenses/by/2.0/
Document Type: 
Article
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