Machine learning and deep learning predictive models for long-term prognosis in patients with chronic obstructive pulmonary disease: a systematic review and meta …

LA Smith, L Oakden-Rayner, A Bird, M Zeng… - The Lancet Digital …, 2023 - thelancet.com
Background Machine learning and deep learning models have been increasingly used to
predict long-term disease progression in patients with chronic obstructive pulmonary …

A tutorial-based survey on feature selection: Recent advancements on feature selection

A Moslemi - Engineering Applications of Artificial Intelligence, 2023 - Elsevier
Curse of dimensionality is known as big challenges in data mining, pattern recognition,
computer vison and machine learning in recent years. Feature selection and feature …

Unsupervised feature selection using sparse manifold learning: Auto-encoder approach

A Moslemi, M Jamshidi - Information Processing & Management, 2025 - Elsevier
Feature selection techniques are widely being used as a preprocessing step to train
machine learning algorithms to circumvent the curse of dimensionality, overfitting, and …

Comparison of feature selection methods and machine learning classifiers for predicting chronic obstructive pulmonary disease using texture-based CT lung radiomic …

K Makimoto, R Au, A Moslemi, JC Hogg, J Bourbeau… - Academic …, 2023 - Elsevier
Rationale Texture-based radiomics analysis of lung computed tomography (CT) images has
been shown to predict chronic obstructive pulmonary disease (COPD) status using machine …

Subspace learning using structure learning and non-convex regularization: Hybrid technique with mushroom reproduction optimization in gene selection

A Moslemi, M Bidar, A Ahmadian - Computers in Biology and Medicine, 2023 - Elsevier
Gene selection as a problem with high dimensions has drawn considerable attention in
machine learning and computational biology over the past decade. In the field of gene …

Attention-guided multiple instance learning for COPD identification: To combine the intensity and morphology

Y Wu, S Qi, J Feng, R Chang, H Pang, J Hou… - Biocybernetics and …, 2023 - Elsevier
Chronic obstructive pulmonary disease (COPD) is a complex and multi-component
respiratory disease. Computed tomography (CT) images can characterize lesions in COPD …

Machine learning for prediction of cardiovascular disease and respiratory disease: a review

G Parashar, A Chaudhary, D Pandey - SN Computer Science, 2024 - Springer
Cardiovascular (CVD) and respiratory diseases (RD) have been in the active domain for
machine learning (ML) researchers as these diseases significantly contribute to mortality in …

Early Diagnosis of High-Risk Chronic Obstructive Pulmonary Disease Based on Quantitative High-Resolution Computed Tomography Measurements

W Zhang, Y Zhao, Y Tian, X Liang… - International Journal of …, 2023 - Taylor & Francis
Purpose Quantitative computed tomography (QCT) techniques, focusing on airway anatomy
and emphysema, may help to detect early structural changes of COPD disease. This …

Are CT-based exacerbation prediction models ready for use in chronic obstructive pulmonary disease?

K Makimoto, M Kirby - The Lancet Digital Health, 2023 - thelancet.com
Quantitative CT imaging extracts information that reflects the severity, degree of change, or
status of a disease. 1 There is growing interest, and scientific evidence, for the use of …

[HTML][HTML] Classifying Future Healthcare Utilization in COPD Using Quantitative CT Lung Imaging and Two-Step Feature Selection via Sparse Subspace Learning with …

A Moslemi, CJ Hague, JC Hogg, J Bourbeau… - Academic …, 2024 - Elsevier
Rationale Although numerous candidate features exist for predicting risk of higher risk of
healthcare utilization in patients with chronic obstructive pulmonary disease (COPD), the …