Implications of Classification Models for Patients with Chronic Obstructive Pulmonary Disease

Abstract

Machine learning-based prediction models have the potential to revamp various industries, and one such promising area is healthcare. This study demonstrates the potential impact of machine learning in healthcare, particularly in managing patients with Chronic Obstructive Pulmonary Disease (COPD). The experimental results showcase the remarkable performance of machine learning models, surpassing doctors' predictions for COPD patients. Among the evaluated models, the Gradient Boosted Decision Tree classifier emerges as the top performer, displaying exceptional classification accuracy, precision, recall, and F1-Score compared to doctors' experience. Notably, the comparison between the best machine learning model and doctors' predictions reveals an interesting pattern: machine learning models tend to be more conservative, resulting in an increased probability of patient recovery.