Risk Factors and Comorbidities Associated to Cardiovascular Disease in Qatar: A Machine Learning Based Case-Control Study
<p dir="ltr">Cardiovascular disease (CVD) is reported to be the leading cause of mortality in the middle eastern countries, including Qatar. But no comprehensive study has been conducted on the Qatar specific CVD risk factors identification. The objective of this case-control study w...
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| مؤلفون آخرون: | , , , , |
| منشور في: |
2021
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إضافة وسم
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| _version_ | 1864513560429723648 |
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| author | Hamada R. H. Al-Absi (16726299) |
| author2 | Mahmoud Ahmed Refaee (16896423) Atiq Ur Rehman (8843024) Mohammad Tariqul Islam (7854059) Samir Brahim Belhaouari (9427347) Tanvir Alam (638619) |
| author2_role | author author author author author |
| author_facet | Hamada R. H. Al-Absi (16726299) Mahmoud Ahmed Refaee (16896423) Atiq Ur Rehman (8843024) Mohammad Tariqul Islam (7854059) Samir Brahim Belhaouari (9427347) Tanvir Alam (638619) |
| author_role | author |
| dc.creator.none.fl_str_mv | Hamada R. H. Al-Absi (16726299) Mahmoud Ahmed Refaee (16896423) Atiq Ur Rehman (8843024) Mohammad Tariqul Islam (7854059) Samir Brahim Belhaouari (9427347) Tanvir Alam (638619) |
| dc.date.none.fl_str_mv | 2021-02-15T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2021.3059469 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Risk_Factors_and_Comorbidities_Associated_to_Cardiovascular_Disease_in_Qatar_A_Machine_Learning_Based_Case-Control_Study/24049302 |
| 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 Clinical sciences Health sciences Health services and systems Information and computing sciences Machine learning Diabetes Obesity Hypertension Biological system modeling Diseases Lipidomics Particle measurements Cardiovascular disease Coronary heart disease Cerebrovascular disease Risk factor Machine learning Qatar Biobank (QBB) Qatar |
| dc.title.none.fl_str_mv | Risk Factors and Comorbidities Associated to Cardiovascular Disease in Qatar: A Machine Learning Based Case-Control Study |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Cardiovascular disease (CVD) is reported to be the leading cause of mortality in the middle eastern countries, including Qatar. But no comprehensive study has been conducted on the Qatar specific CVD risk factors identification. The objective of this case-control study was to develop machine learning (ML) model distinguishing healthy individuals from people having CVD, which could ultimately reveal the list of potential risk factors associated to CVD in Qatar. To the best of our knowledge, this study considered the largest collection of biomedical measurements representing the anthropometric measurements, clinical biomarkers, bioimpedance, spirometry, VICORDER readings, and behavioral factors of the CVD group from Qatar Biobank (QBB). CatBoost model achieved 93% accuracy, thereby outperforming the existing model for the same purpose. Interestingly, combining multimodal datasets into the proposed ML model outperformed the ML model built upon currently known risk factors for CVD, emphasizing the importance of incorporating other clinical biomarkers into consideration for CVD diagnosis plan. The ablation study on the multimodal dataset from QBB revealed that physio-clinical and bioimpedance measurements have the most distinguishing power to classify these two groups irrespective of gender and age of the participants. Multiple feature subset selection techniques confirmed known CVD risk factors (blood pressure, lipid profile, smoking, sedentary life, and diabetes), and identified potential novel risk factors linked to CVD-related comorbidities such as renal disorder (e.g., creatinine, uric acid, homocysteine, albumin), atherosclerosis (intima media thickness), hypercoagulable state (fibrinogen), and liver function (e.g., alkaline phosphate, gamma-glutamyl transferase). Moreover, the inclusion of the proposed novel factors into the ML model provides better performance than the model with traditional known risk factors for CVD. The association of the proposed risk factors and comorbidities are required to be investigated in clinical setup to understand their role in CVD better.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2021.3059469" target="_blank">https://dx.doi.org/10.1109/access.2021.3059469</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_f861451ce3e55dcc10c9149f7958f27e |
| identifier_str_mv | 10.1109/access.2021.3059469 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24049302 |
| publishDate | 2021 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Risk Factors and Comorbidities Associated to Cardiovascular Disease in Qatar: A Machine Learning Based Case-Control StudyHamada R. H. Al-Absi (16726299)Mahmoud Ahmed Refaee (16896423)Atiq Ur Rehman (8843024)Mohammad Tariqul Islam (7854059)Samir Brahim Belhaouari (9427347)Tanvir Alam (638619)Biomedical and clinical sciencesCardiovascular medicine and haematologyClinical sciencesHealth sciencesHealth services and systemsInformation and computing sciencesMachine learningDiabetesObesityHypertensionBiological system modelingDiseasesLipidomicsParticle measurementsCardiovascular diseaseCoronary heart diseaseCerebrovascular diseaseRisk factorMachine learningQatar Biobank (QBB)Qatar<p dir="ltr">Cardiovascular disease (CVD) is reported to be the leading cause of mortality in the middle eastern countries, including Qatar. But no comprehensive study has been conducted on the Qatar specific CVD risk factors identification. The objective of this case-control study was to develop machine learning (ML) model distinguishing healthy individuals from people having CVD, which could ultimately reveal the list of potential risk factors associated to CVD in Qatar. To the best of our knowledge, this study considered the largest collection of biomedical measurements representing the anthropometric measurements, clinical biomarkers, bioimpedance, spirometry, VICORDER readings, and behavioral factors of the CVD group from Qatar Biobank (QBB). CatBoost model achieved 93% accuracy, thereby outperforming the existing model for the same purpose. Interestingly, combining multimodal datasets into the proposed ML model outperformed the ML model built upon currently known risk factors for CVD, emphasizing the importance of incorporating other clinical biomarkers into consideration for CVD diagnosis plan. The ablation study on the multimodal dataset from QBB revealed that physio-clinical and bioimpedance measurements have the most distinguishing power to classify these two groups irrespective of gender and age of the participants. Multiple feature subset selection techniques confirmed known CVD risk factors (blood pressure, lipid profile, smoking, sedentary life, and diabetes), and identified potential novel risk factors linked to CVD-related comorbidities such as renal disorder (e.g., creatinine, uric acid, homocysteine, albumin), atherosclerosis (intima media thickness), hypercoagulable state (fibrinogen), and liver function (e.g., alkaline phosphate, gamma-glutamyl transferase). Moreover, the inclusion of the proposed novel factors into the ML model provides better performance than the model with traditional known risk factors for CVD. The association of the proposed risk factors and comorbidities are required to be investigated in clinical setup to understand their role in CVD better.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2021.3059469" target="_blank">https://dx.doi.org/10.1109/access.2021.3059469</a></p>2021-02-15T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2021.3059469https://figshare.com/articles/journal_contribution/Risk_Factors_and_Comorbidities_Associated_to_Cardiovascular_Disease_in_Qatar_A_Machine_Learning_Based_Case-Control_Study/24049302CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/240493022021-02-15T00:00:00Z |
| spellingShingle | Risk Factors and Comorbidities Associated to Cardiovascular Disease in Qatar: A Machine Learning Based Case-Control Study Hamada R. H. Al-Absi (16726299) Biomedical and clinical sciences Cardiovascular medicine and haematology Clinical sciences Health sciences Health services and systems Information and computing sciences Machine learning Diabetes Obesity Hypertension Biological system modeling Diseases Lipidomics Particle measurements Cardiovascular disease Coronary heart disease Cerebrovascular disease Risk factor Machine learning Qatar Biobank (QBB) Qatar |
| status_str | publishedVersion |
| title | Risk Factors and Comorbidities Associated to Cardiovascular Disease in Qatar: A Machine Learning Based Case-Control Study |
| title_full | Risk Factors and Comorbidities Associated to Cardiovascular Disease in Qatar: A Machine Learning Based Case-Control Study |
| title_fullStr | Risk Factors and Comorbidities Associated to Cardiovascular Disease in Qatar: A Machine Learning Based Case-Control Study |
| title_full_unstemmed | Risk Factors and Comorbidities Associated to Cardiovascular Disease in Qatar: A Machine Learning Based Case-Control Study |
| title_short | Risk Factors and Comorbidities Associated to Cardiovascular Disease in Qatar: A Machine Learning Based Case-Control Study |
| title_sort | Risk Factors and Comorbidities Associated to Cardiovascular Disease in Qatar: A Machine Learning Based Case-Control Study |
| topic | Biomedical and clinical sciences Cardiovascular medicine and haematology Clinical sciences Health sciences Health services and systems Information and computing sciences Machine learning Diabetes Obesity Hypertension Biological system modeling Diseases Lipidomics Particle measurements Cardiovascular disease Coronary heart disease Cerebrovascular disease Risk factor Machine learning Qatar Biobank (QBB) Qatar |