| Title: |
Machine Learning based Model Reveals the Metabolites Involved in Coronary Artery Disease |
| Authors: |
Fathima Lamya; Muhammad Arif; Mahbuba Rahman; Abdul Rehman Zar Gul; Tanvir Alam |
| Source: |
Biomedical Engineering and Computational Biology, Vol 16 (2025) |
| Publisher Information: |
SAGE Publishing, 2025. |
| Publication Year: |
2025 |
| Collection: |
LCC:Biology (General) |
| Subject Terms: |
Biology (General); QH301-705.5 |
| Description: |
Introduction: Coronary artery disease (CAD) is a major global cause of morbidity and mortality. Therefore, advances in early identification and individualized treatment plans are crucial. Methods: This article presents machine learning (ML) based model that can recognize metabolomic compounds associated with CAD in the Qatari population for the early detection of CAD. We also identified statistically significant metabolic profiles and potential biomarkers using ML methods. Results: Among all ML models, artificial neural network (ANN) outstands all with an accuracy of 91.67%, recall of 80.0%, and specificity of 100%. The results show that 173 metabolites ( P |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
1179-5972 |
| Relation: |
https://doaj.org/toc/1179-5972 |
| DOI: |
10.1177/11795972251352014 |
| Access URL: |
https://doaj.org/article/cce08c0d3083417ea54e00aeb56715c1 |
| Accession Number: |
edsdoj.08c0d3083417ea54e00aeb56715c1 |
| Database: |
Directory of Open Access Journals |