A Comprehensive Review on the Application of Artificial Intelligence in Chronic Obstructive Pulmonary Disease (COPD) Management

Xu, Yiqing; Long, Zalizah Awang; Setyohadi, Djoko Budiyanto

doi:10.1109/imcom60618.2024.10418416

2024 18th International Conference on Ubiquitous Information Management and Communication (IMCOM) 2024

DOI: 10.1109/imcom60618.2024.10418416

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A Comprehensive Review on the Application of Artificial Intelligence in Chronic Obstructive Pulmonary Disease (COPD) Management

Yiqing Xu,

Zalizah Awang Long,

Djoko Budiyanto Setyohadi

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2024

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Machine Learning for Enhanced COPD Diagnosis: A Comparative Analysis of Classification Algorithms

Elashmawi,

Djellal,

Sheta

et al. 2024

Diagnostics

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Background: In the United States, chronic obstructive pulmonary disease (COPD) is a significant cause of mortality. As far as we know, it is a chronic, inflammatory lung condition that cuts off airflow to the lungs. Many symptoms have been reported for such a disease: breathing problems, coughing, wheezing, and mucus production. Patients with COPD might be at risk, since they are more susceptible to heart disease and lung cancer. Methods: This study reviews COPD diagnosis utilizing various machine learning (ML) classifiers, such as Logistic Regression (LR), Gradient Boosting Classifier (GBC), Support Vector Machine (SVM), Gaussian Naïve Bayes (GNB), Random Forest Classifier (RFC), K-Nearest Neighbors Classifier (KNC), Decision Tree (DT), and Artificial Neural Network (ANN). These models were applied to a dataset comprising 1603 patients after being referred for a pulmonary function test. Results: The RFC has achieved superior accuracy, reaching up to 82.06% in training and 70.47% in testing. Furthermore, it achieved a maximum F score in training and testing with an ROC value of 0.0.82. Conclusions: The results obtained with the utilized ML models align with previous work in the field, with accuracies ranging from 67.81% to 82.06% in training and from 66.73% to 71.46% in testing.

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Machine Learning for Enhanced COPD Diagnosis: A Comparative Analysis of Classification Algorithms

Elashmawi,

Djellal,

Sheta

et al. 2024

Diagnostics

View full text Add to dashboard Cite

show abstract

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A Comprehensive Review on the Application of Artificial Intelligence in Chronic Obstructive Pulmonary Disease (COPD) Management

Cited by 1 publication

References 74 publications

Machine Learning for Enhanced COPD Diagnosis: A Comparative Analysis of Classification Algorithms

Machine Learning for Enhanced COPD Diagnosis: A Comparative Analysis of Classification Algorithms

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