Implications of Classification Models for Patients with Chronic Obstructive Pulmonary Disease - Kang, Mengyao; Zhao, Jiawei; Farid, Farnaz (Artificial Intelligence and Applications, 2024)
Machine learning (ML)-based prediction models have the potential to revamp various industries, and one such promising area is healthcare. This study demonstrates the potential impact of ML on healthcare, particularly in managing patients with chronic obstructive pulmonary disease (COPD). The experimental results showcase the remarkable performance of ML 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 ML model and doctors’ predictions reveals an interesting pattern: ML models tend to be more conservative, resulting in an increased probability of patient recovery. Received: 25 July 2023 | Revised: 27 August 2023 | Accepted: 11 September 2023 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in PLOS ONE at https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0188532.