Fractional dynamics foster deep learning of COPD stage prediction

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death worldwide, usually associated with smoking and environmental occupational exposures. Prior studies have shown that current COPD diagnosis (i.e., spirometry test) can be unreliable because the test can be difficult to do and depends on an adequate effort from the testee and supervision of the testor. Moreover, the extensive early detection and diagnosis of COPD is challenging. We address the COPD detection problem by constructing two novel COPD physiological signals datasets (4432 medical records from 54 patients in the WestRo COPD dataset and 13824 medical records from 534 patients in the WestRo Porti COPD dataset), demonstrating their complex coupled fractal dynamical characteristics, and performing a rigorous fractional-order dynamics deep learning analysis to diagnose COPD with high accuracy. We find 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). We exploit these fractional signatures to develop and train a deep neural network that predicts the suspected patients' COPD stages based on the input features (such as thorax breathing effort, respiratory rate, or oxygen saturation levels). We show that our COPD diagnostics method (fractional dynamic deep learning model) achieves a high prediction accuracy (98.66% 0.45%) on WestRo COPD dataset and can serve as an excellent and robust alternative to traditional spirometry-based medical diagnosis. Our fractional dynamic deep learning model (FDDLM) for COPD diagnosis also presents high prediction accuracy when validated by a dataset with different physiological signals recorded (i.e., 94.01%