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

Abstract

Introduction: Chronic Obstructive Pulmonary Disease (COPD), is a condition caused by damage to the
airways or other parts of the lung that blocks airflow and makes it hard to breathe [1]. COPD is the third
leading cause of death worldwide, and the seventh leading cause of poor health worldwide [2]. Studies
have shown that 20–86% of people with COPD worldwide may be undiagnosed [3]. As there is currently
no cure for COPD, early detection is the best option. Current Machine Learning (ML) models focus on
using chest images (CT or X-ray scans) to detect COPD; however, the scanning process can be unsafe for
patients with COPD [4].
Methods: This study utilized an open data set containing various physical tests of 100 patients with
COPD. To train this model, the Random Forest (RF) classifier was used. The accuracy was then plotted
on a graph.
Results: The Random Forest classifier was able to achieve an accuracy of 92.41% with a perfect recall
value of 1.00. This recall value indicates that the Random Forest classifier was able to correctly diagnose
all 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 individuals
have been reported to have a higher prevalence of COPD [5]. By developing a dataset that accounts for
race, we will be able to obtain a more accurate diagnosis.