Machine Learning based Model Reveals the Metabolites Involved in Coronary Artery Disease
<h3>Introduction</h3><p dir="ltr">Coronary artery disease (CAD) is a major global cause of morbidity and mortality. Therefore, advances in early identification and individualized treatment plans are crucial.</p><h3>Methods</h3><p dir="ltr"&g...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , , , |
| منشور في: |
2025
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1864513533177233408 |
|---|---|
| author | Fathima Lamya (22565261) |
| author2 | Muhammad Arif (769250) Mahbuba Rahman (198499) Abdul Rehman Zar Gul (14442126) Tanvir Alam (638619) |
| author2_role | author author author author |
| author_facet | Fathima Lamya (22565261) Muhammad Arif (769250) Mahbuba Rahman (198499) Abdul Rehman Zar Gul (14442126) Tanvir Alam (638619) |
| author_role | author |
| dc.creator.none.fl_str_mv | Fathima Lamya (22565261) Muhammad Arif (769250) Mahbuba Rahman (198499) Abdul Rehman Zar Gul (14442126) Tanvir Alam (638619) |
| dc.date.none.fl_str_mv | 2025-07-08T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1177/11795972251352014 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Machine_Learning_based_Model_Reveals_the_Metabolites_Involved_in_Coronary_Artery_Disease/30541175 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Cardiovascular medicine and haematology Health sciences Health services and systems Information and computing sciences Machine learning coronary artery disease machine learning metabolomics Qatar Biobank metabolites |
| dc.title.none.fl_str_mv | Machine Learning based Model Reveals the Metabolites Involved in Coronary Artery Disease |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <h3>Introduction</h3><p dir="ltr">Coronary artery disease (CAD) is a major global cause of morbidity and mortality. Therefore, advances in early identification and individualized treatment plans are crucial.</p><h3>Methods</h3><p dir="ltr">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.</p><h3>Results</h3><p dir="ltr">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 < .05) are significantly associated with CAD. Of these metabolites, the majority (95/173, 54.91%) were high in CAD patients, while 45.09% (78/173) were high in the control group. Two metabolites 2-hydroxyhippurate (salicylurate) and salicylate were notably higher in CAD patients compared to the control group. Conversely, 4 metabolites, cholate, 3-hydroxybutyrate (BHBA), 4-allyl catechol sulfate, and indolepropionate, showed relatively higher level in the control group.</p><h3>Conclusion</h3><p dir="ltr">We believe our study will support in advancing personalized diagnosis plan for CAD patients by considering the metabolites involved in CAD.</p><h2>Other Information</h2><p dir="ltr">Published in: Biomedical Engineering and Computational Biology<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1177/11795972251352014" target="_blank">https://dx.doi.org/10.1177/11795972251352014</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_005f12aee81302624c348c29a0bedffb |
| identifier_str_mv | 10.1177/11795972251352014 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30541175 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Machine Learning based Model Reveals the Metabolites Involved in Coronary Artery DiseaseFathima Lamya (22565261)Muhammad Arif (769250)Mahbuba Rahman (198499)Abdul Rehman Zar Gul (14442126)Tanvir Alam (638619)Biomedical and clinical sciencesCardiovascular medicine and haematologyHealth sciencesHealth services and systemsInformation and computing sciencesMachine learningcoronary artery diseasemachine learningmetabolomicsQatar Biobankmetabolites<h3>Introduction</h3><p dir="ltr">Coronary artery disease (CAD) is a major global cause of morbidity and mortality. Therefore, advances in early identification and individualized treatment plans are crucial.</p><h3>Methods</h3><p dir="ltr">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.</p><h3>Results</h3><p dir="ltr">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 < .05) are significantly associated with CAD. Of these metabolites, the majority (95/173, 54.91%) were high in CAD patients, while 45.09% (78/173) were high in the control group. Two metabolites 2-hydroxyhippurate (salicylurate) and salicylate were notably higher in CAD patients compared to the control group. Conversely, 4 metabolites, cholate, 3-hydroxybutyrate (BHBA), 4-allyl catechol sulfate, and indolepropionate, showed relatively higher level in the control group.</p><h3>Conclusion</h3><p dir="ltr">We believe our study will support in advancing personalized diagnosis plan for CAD patients by considering the metabolites involved in CAD.</p><h2>Other Information</h2><p dir="ltr">Published in: Biomedical Engineering and Computational Biology<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1177/11795972251352014" target="_blank">https://dx.doi.org/10.1177/11795972251352014</a></p>2025-07-08T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1177/11795972251352014https://figshare.com/articles/journal_contribution/Machine_Learning_based_Model_Reveals_the_Metabolites_Involved_in_Coronary_Artery_Disease/30541175CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/305411752025-07-08T03:00:00Z |
| spellingShingle | Machine Learning based Model Reveals the Metabolites Involved in Coronary Artery Disease Fathima Lamya (22565261) Biomedical and clinical sciences Cardiovascular medicine and haematology Health sciences Health services and systems Information and computing sciences Machine learning coronary artery disease machine learning metabolomics Qatar Biobank metabolites |
| status_str | publishedVersion |
| title | Machine Learning based Model Reveals the Metabolites Involved in Coronary Artery Disease |
| title_full | Machine Learning based Model Reveals the Metabolites Involved in Coronary Artery Disease |
| title_fullStr | Machine Learning based Model Reveals the Metabolites Involved in Coronary Artery Disease |
| title_full_unstemmed | Machine Learning based Model Reveals the Metabolites Involved in Coronary Artery Disease |
| title_short | Machine Learning based Model Reveals the Metabolites Involved in Coronary Artery Disease |
| title_sort | Machine Learning based Model Reveals the Metabolites Involved in Coronary Artery Disease |
| topic | Biomedical and clinical sciences Cardiovascular medicine and haematology Health sciences Health services and systems Information and computing sciences Machine learning coronary artery disease machine learning metabolomics Qatar Biobank metabolites |