Developing a Novel, Highly Accurate, Chronic Obstructive Pulmonary Disease (COPD) Machine Learning (ML) Model

Introduction: Chronic Obstructive Pulmonary Disease (COPD), is a condition caused by damage to theairways or other parts of the lung that blocks airflow and makes it hard to breathe [1]. COPD is the thirdleading cause of death worldwide, and the seventh leading cause of poor health worldwide [2]. Studieshave shown that 20–86% of people with COPD worldwide may be undiagnosed [3]. As there is currentlyno cure for COPD, early detection is the best option. Current Machine Learning (ML) models focus onusing chest images (CT or X-ray scans) to detect COPD; however, the scanning process can be unsafe forpatients with COPD [4].Methods: This study utilized an open data set containing various physical tests of 100 patients withCOPD. To train this model, the Random Forest (RF) classifier was used. The accuracy was then plottedon a graph.Results: The Random Forest classifier was able to achieve an accuracy of 92.41% with a perfect recallvalue of 1.00. This recall value indicates that the Random Forest classifier was able to correctly diagnoseall of the patients with COPD in this dataset.Discussion: This study developed a novel ML model that can accurately provide a diagnosis for COPD.Further studies could use this code with a larger dataset to obtain a higher accuracy. White individualshave been reported to have a higher prevalence of COPD [5]. By developing a dataset that accounts forrace, we will be able to obtain a more accurate diagnosis.