Artificial intelligence (AI) and machine learning (ML) techniques have emerged as effective methods for mining and integrating large-scale, heterogeneous medical data for clinical practice, and several AI and ML methods have recently been applied to asthma and COPD.

Various machine learning classifiers are used to diagnose COPD, including LR, SVM, GBC, GNB, RFC, KNC, ANN, and DT. Each method has unique advantages and drawbacks, and selecting the most suitable approach hinges on factors such as the problem’s characteristics, dataset size, data quality, and available computational resources.

We selected 30 clinical, spirometric, and imaging features as inputs for a random survival forest. We used top features in a Cox regression to create a machine learning mortality prediction (MLMP) in COPD model and also assessed the performance of other statistical and machine learning models.

Research question: We hypothesized that applying machine learning to clinical and quantitative CT imaging features would improve mortality prediction in COPD. Study design and methods: We selected 30 clinical, spirometric, and imaging features as inputs for a random survival forest.

The AMGNN with radiomics and 3D CNN features achieves the best performance at 89.7 % of accuracy, 90.9 % of precision, 89.5 % of F1-score, and 95.8 % of AUC compared to six classic machine learning (ML) classifiers. Our proposed approach demonstrates high accuracy in detecting the stage of COPD using both IN and EX chest CT images.

Jun 13, 2024 · We proposed a new COPD diagnostic system that models the curvature of the expiratory flow-volume trace through a mathematical model and feeds the coefficients of the model to a machine-learning technique to classify patients between those …

Jun 24, 2022 · In this paper, a novel method based on graph convolution network (GCN) for early detection of COPD is proposed, which uses small and weakly labeled chest computed tomography image data from the publicly available Danish Lung Cancer Screening Trial database.

In this paper we discussed prediction of CODP using machine learning approach. The early identification and prediction of lung diseases have become a necessity in the research, as it can facilitate the subsequent clinical management of patients.

Feb 7, 2021 · Different machine learning and deep learning models are trained and tested on the collected dataset. The findings are promising, with a high accuracy score of 100% for COPD detection.

This study, with the objective of a contemporary literature review, has focused on the earlier contributions of critical models of machine learning for COPD detection.