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In-Vitro Classification of Saliva Samples of COPD Patients and Healthy Controls Using Machine Learning Tools

  • Chronic Obstructive Pulmonary Disease (COPD) is a life-threatening lung disease and a major cause of morbidity and mortality worldwide. Although a curative therapy has yet to be found, permanent monitoring of biomarkers that reflect the disease progression plays a pivotal role for the effective management of COPD. The accurate examination of respiratory tract fluids like saliva is a promising approach for staging the disease and predicting its upcoming exacerbations in a Point-of-Care (PoC) environment. Nonetheless, this approach is only feasible by concurrent consideration of patients' demographic and medical parameters. Therefore, Machine Learning (ML) tools are necessary for the comprehensive recognition of COPD in a PoC setting. As a result, the objective of this work was to implement ML tools on the data acquired from characterizing saliva samples of COPD patients and healthy controls for classification purposes. First, a permittivity biosensor was used to characterize dielectric properties of saliva samples and, subsequently, MLChronic Obstructive Pulmonary Disease (COPD) is a life-threatening lung disease and a major cause of morbidity and mortality worldwide. Although a curative therapy has yet to be found, permanent monitoring of biomarkers that reflect the disease progression plays a pivotal role for the effective management of COPD. The accurate examination of respiratory tract fluids like saliva is a promising approach for staging the disease and predicting its upcoming exacerbations in a Point-of-Care (PoC) environment. Nonetheless, this approach is only feasible by concurrent consideration of patients' demographic and medical parameters. Therefore, Machine Learning (ML) tools are necessary for the comprehensive recognition of COPD in a PoC setting. As a result, the objective of this work was to implement ML tools on the data acquired from characterizing saliva samples of COPD patients and healthy controls for classification purposes. First, a permittivity biosensor was used to characterize dielectric properties of saliva samples and, subsequently, ML tools were applied on the acquired data for classification. The XGBoost gradient boosting algorithm provided a high classification accuracy of 91.25%, making it a promising model for COPD recognition. Integration of this model on a neuromorphic chip, in the future, will enable the real-time detection of COPD in PoC, with low energy consumption and high patient privacy.show moreshow less

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Metadaten
Author: Pouya Soltani Zarrin, Niels Rockendorf, Christian WengerORCiD
DOI:https://doi.org/10.1109/ACCESS.2020.3023971
ISSN:2169-3536
Title of the source (English):IEEE Access
Document Type:Scientific journal article peer-reviewed
Language:English
Year of publication:2020
Tag:COPD; Machine learning; Point of care
Volume/Year:Vol. 8
First Page:168053
Last Page:168060
Faculty/Chair:Fakultät 1 MINT - Mathematik, Informatik, Physik, Elektro- und Informationstechnik / FG Halbleitermaterialien
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