Fractional dynamics foster deep learning of COPD stage prediction Article Swipe

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Chenzhong Yin , Mihai Udrescu , Gaurav Gupta , Mingxi Cheng , Andrei Lihu , Lucreţia Udrescu , Paul Bogdan , David M. Mannino , Ştefan Mihăicuţă · YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2303.07537

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death worldwide. Current COPD diagnosis (i.e., spirometry) could be unreliable because the test depends on an adequate effort from the tester and testee. Moreover, the early diagnosis of COPD is challenging. We address COPD detection by constructing two novel physiological signals datasets (4432 records from 54 patients in the WestRo COPD dataset and 13824 medical records from 534 patients in the WestRo Porti COPD dataset). The authors demonstrate their complex coupled fractal dynamical characteristics and perform a fractional-order dynamics deep learning analysis to diagnose COPD. The authors found that the fractional-order dynamical modeling can extract distinguishing signatures from the physiological signals across patients with all COPD stages from stage 0 (healthy) to stage 4 (very severe). They use the fractional signatures to develop and train a deep neural network that predicts COPD stages based on the input features (such as thorax breathing effort, respiratory rate, or oxygen saturation). The authors show that the fractional dynamic deep learning model (FDDLM) achieves a COPD prediction accuracy of 98.66% and can serve as a robust alternative to spirometry. The FDDLM also has high accuracy when validated on a dataset with different physiological signals.

Type

preprint

Language

en

Landing Page

http://arxiv.org/abs/2303.07537

PDF

https://arxiv.org/pdf/2303.07537

OA Status

green

Related Works

10

OpenAlex ID

https://openalex.org/W4327486487

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OpenAlex ID

https://openalex.org/W4327486487

Canonical identifier for this work in OpenAlex

DOI

https://doi.org/10.48550/arxiv.2303.07537

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Title

Fractional dynamics foster deep learning of COPD stage prediction

Work title

Type

preprint

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Language

en

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Publication year

2023

Year of publication

Publication date

2023-03-13

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Authors

Chenzhong Yin, Mihai Udrescu, Gaurav Gupta, Mingxi Cheng, Andrei Lihu, Lucreţia Udrescu, Paul Bogdan, David M. Mannino, Ştefan Mihăicuţă

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Landing page

https://arxiv.org/abs/2303.07537

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PDF URL

https://arxiv.org/pdf/2303.07537

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Open access

Yes

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OA status

green

Open access status per OpenAlex

OA URL

https://arxiv.org/pdf/2303.07537

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Concepts

COPD, Spirometry, Medicine, Artificial intelligence, Computer science, Deep learning, Machine learning, Physical therapy, Internal medicine, Asthma

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Cited by

0

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Related works (count)

10

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