Next Article in Journal
Cell Adhesion Molecule 1 (CADM1) Is an Independent Prognostic Factor in Patients with Cutaneous Squamous Cell Carcinoma
Previous Article in Journal
How Can Rotational Thromboelastometry as a Point-of-Care Method Be Useful for the Management of Secondary Thromboprophylaxis in High-Risk Pregnant Patients?
Open AccessArticle

Forecast the Exacerbation in Patients of Chronic Obstructive Pulmonary Disease with Clinical Indicators Using Machine Learning Techniques

1
Institute of Digital Anti-Aging Healthcare, Inje University, Gimhae 50834, Korea
2
Department of Physical Medicine and Rehabilitation, Haeundae Paik Hospital, Inje University College of Medicine, Busan 48108, Korea
3
Department of Internal Medicine, Haeundae Paik Hospital, Inje University College of Medicine, Busan 48108, Korea
4
Department of Computer Science & Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Korea
5
College of AI Convergence/Institute of Digital Anti-Aging Healthcare/u-HARC, Inje University, Gimhae 50834, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Dechang Chen
Diagnostics 2021, 11(5), 829; https://doi.org/10.3390/diagnostics11050829
Received: 20 March 2021 / Revised: 26 April 2021 / Accepted: 1 May 2021 / Published: 4 May 2021
(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)
Preventing exacerbation and seeking to determine the severity of the disease during the hospitalization of chronic obstructive pulmonary disease (COPD) patients is a crucial global initiative for chronic obstructive lung disease (GOLD); this option is available only for stable-phase patients. Recently, the assessment and prediction techniques that are used have been determined to be inadequate for acute exacerbation of chronic obstructive pulmonary disease patients. To magnify the monitoring and treatment of acute exacerbation COPD patients, we need to rely on the AI system, because traditional methods take a long time for the prognosis of the disease. Machine-learning techniques have shown the capacity to be effectively used in crucial healthcare applications. In this paper, we propose a voting ensemble classifier with 24 features to identify the severity of chronic obstructive pulmonary disease patients. In our study, we applied five machine-learning classifiers, namely random forests (RF), support vector machine (SVM), gradient boosting machine (GBM), XGboost (XGB), and K-nearest neighbor (KNN). These classifiers were trained with a set of 24 features. After that, we combined their results with a soft voting ensemble (SVE) method. Consequently, we found performance measures with an accuracy of 91.0849%, a precision of 90.7725%, a recall of 91.3607%, an F-measure of 91.0656%, and an AUC score of 96.8656%, respectively. Our result shows that the SVE classifier with the proposed twenty-four features outperformed regular machine-learning-based methods for chronic obstructive pulmonary disease (COPD) patients. The SVE classifier helps respiratory physicians to estimate the severity of COPD patients in the early stage, consequently guiding the cure strategy and helps the prognosis of COPD patients. View Full-Text
Keywords: chronic obstructive pulmonary disease (COPD); machine learning; features set; disease severity; prediction models chronic obstructive pulmonary disease (COPD); machine learning; features set; disease severity; prediction models
Show Figures

Figure 1

MDPI and ACS Style

Hussain, A.; Choi, H.-E.; Kim, H.-J.; Aich, S.; Saqlain, M.; Kim, H.-C. Forecast the Exacerbation in Patients of Chronic Obstructive Pulmonary Disease with Clinical Indicators Using Machine Learning Techniques. Diagnostics 2021, 11, 829. https://doi.org/10.3390/diagnostics11050829

AMA Style

Hussain A, Choi H-E, Kim H-J, Aich S, Saqlain M, Kim H-C. Forecast the Exacerbation in Patients of Chronic Obstructive Pulmonary Disease with Clinical Indicators Using Machine Learning Techniques. Diagnostics. 2021; 11(5):829. https://doi.org/10.3390/diagnostics11050829

Chicago/Turabian Style

Hussain, Ali; Choi, Hee-Eun; Kim, Hyo-Jung; Aich, Satyabrata; Saqlain, Muhammad; Kim, Hee-Cheol. 2021. "Forecast the Exacerbation in Patients of Chronic Obstructive Pulmonary Disease with Clinical Indicators Using Machine Learning Techniques" Diagnostics 11, no. 5: 829. https://doi.org/10.3390/diagnostics11050829

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop