An ensemble-based machine learning model for predicting type 2 diabetes and its effect on bone health
<h3>Background</h3><p dir="ltr">Diabetes is a chronic condition that can result in many long-term physiological, metabolic, and neurological complications. Therefore, early detection of diabetes would help to determine a proper diagnosis and treatment plan.</p><h...
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2024
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| _version_ | 1864513509799231488 |
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| author | Belqes Alsadi (18695031) |
| author2 | Saleh Musleh (15279190) Hamada R. H. Al-Absi (16726299) Mahmoud Refaee (18695034) Rizwan Qureshi (15279193) Nady El Hajj (18695037) Tanvir Alam (638619) |
| author2_role | author author author author author author |
| author_facet | Belqes Alsadi (18695031) Saleh Musleh (15279190) Hamada R. H. Al-Absi (16726299) Mahmoud Refaee (18695034) Rizwan Qureshi (15279193) Nady El Hajj (18695037) Tanvir Alam (638619) |
| author_role | author |
| dc.creator.none.fl_str_mv | Belqes Alsadi (18695031) Saleh Musleh (15279190) Hamada R. H. Al-Absi (16726299) Mahmoud Refaee (18695034) Rizwan Qureshi (15279193) Nady El Hajj (18695037) Tanvir Alam (638619) |
| dc.date.none.fl_str_mv | 2024-05-29T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1186/s12911-024-02540-0 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/An_ensemble-based_machine_learning_model_for_predicting_type_2_diabetes_and_its_effect_on_bone_health/26404099 |
| 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 Clinical sciences Medical biochemistry and metabolomics Health sciences Health services and systems Information and computing sciences Machine learning Machine learning Diabetes Bone health Dual Energy X-ray Absorptiometry Qatar Biobank (QBB) |
| dc.title.none.fl_str_mv | An ensemble-based machine learning model for predicting type 2 diabetes and its effect on bone health |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <h3>Background</h3><p dir="ltr">Diabetes is a chronic condition that can result in many long-term physiological, metabolic, and neurological complications. Therefore, early detection of diabetes would help to determine a proper diagnosis and treatment plan.</p><h3>Methods</h3><p dir="ltr">In this study, we employed machine learning (ML) based case-control study on a diabetic cohort size of 1000 participants form Qatar Biobank to predict diabetes using clinical and bone health indicators from Dual Energy X-ray Absorptiometry (DXA) machines. ML models were utilized to distinguish diabetes groups from non-diabetes controls. Recursive feature elimination (RFE) was leveraged to identify a subset of features to improve the performance of model. SHAP based analysis was used for the importance of features and support the explainability of the proposed model.</p><h3>Results</h3><p dir="ltr">Ensemble based models XGboost and RF achieved over 84% accuracy for detecting diabetes. After applying RFE, we selected only 20 features which improved the model accuracy to 87.2%. From a clinical standpoint, higher HDL-Cholesterol and Neutrophil levels were observed in the diabetic group, along with lower vitamin B12 and testosterone levels. Lower sodium levels were found in diabetics, potentially stemming from clinical factors including specific medications, hormonal imbalances, unmanaged diabetes. We believe Dapagliflozin prescriptions in Qatar were associated with decreased Gamma Glutamyltransferase and Aspartate Aminotransferase enzyme levels, confirming prior research. We observed that bone area, bone mineral content, and bone mineral density were slightly lower in the Diabetes group across almost all body parts, but the difference against the control group was not statistically significant except in T12, troch and trunk area. No significant negative impact of diabetes progression on bone health was observed over a period of 5-15 yrs in the cohort.</p><h3>Conclusion</h3><p dir="ltr">This study recommends the inclusion of ML model which combines both DXA and clinical data for the early diagnosis of diabetes.</p><h2>Other Information</h2><p dir="ltr">Published in: BMC Medical Informatics and Decision Making<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1186/s12911-024-02540-0" target="_blank">https://dx.doi.org/10.1186/s12911-024-02540-0</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_9c8b3886417baffc76c3b7393ef1b21e |
| identifier_str_mv | 10.1186/s12911-024-02540-0 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26404099 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | An ensemble-based machine learning model for predicting type 2 diabetes and its effect on bone healthBelqes Alsadi (18695031)Saleh Musleh (15279190)Hamada R. H. Al-Absi (16726299)Mahmoud Refaee (18695034)Rizwan Qureshi (15279193)Nady El Hajj (18695037)Tanvir Alam (638619)Biomedical and clinical sciencesClinical sciencesMedical biochemistry and metabolomicsHealth sciencesHealth services and systemsInformation and computing sciencesMachine learningMachine learningDiabetes Bone healthDual Energy X-rayAbsorptiometry Qatar Biobank (QBB)<h3>Background</h3><p dir="ltr">Diabetes is a chronic condition that can result in many long-term physiological, metabolic, and neurological complications. Therefore, early detection of diabetes would help to determine a proper diagnosis and treatment plan.</p><h3>Methods</h3><p dir="ltr">In this study, we employed machine learning (ML) based case-control study on a diabetic cohort size of 1000 participants form Qatar Biobank to predict diabetes using clinical and bone health indicators from Dual Energy X-ray Absorptiometry (DXA) machines. ML models were utilized to distinguish diabetes groups from non-diabetes controls. Recursive feature elimination (RFE) was leveraged to identify a subset of features to improve the performance of model. SHAP based analysis was used for the importance of features and support the explainability of the proposed model.</p><h3>Results</h3><p dir="ltr">Ensemble based models XGboost and RF achieved over 84% accuracy for detecting diabetes. After applying RFE, we selected only 20 features which improved the model accuracy to 87.2%. From a clinical standpoint, higher HDL-Cholesterol and Neutrophil levels were observed in the diabetic group, along with lower vitamin B12 and testosterone levels. Lower sodium levels were found in diabetics, potentially stemming from clinical factors including specific medications, hormonal imbalances, unmanaged diabetes. We believe Dapagliflozin prescriptions in Qatar were associated with decreased Gamma Glutamyltransferase and Aspartate Aminotransferase enzyme levels, confirming prior research. We observed that bone area, bone mineral content, and bone mineral density were slightly lower in the Diabetes group across almost all body parts, but the difference against the control group was not statistically significant except in T12, troch and trunk area. No significant negative impact of diabetes progression on bone health was observed over a period of 5-15 yrs in the cohort.</p><h3>Conclusion</h3><p dir="ltr">This study recommends the inclusion of ML model which combines both DXA and clinical data for the early diagnosis of diabetes.</p><h2>Other Information</h2><p dir="ltr">Published in: BMC Medical Informatics and Decision Making<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1186/s12911-024-02540-0" target="_blank">https://dx.doi.org/10.1186/s12911-024-02540-0</a></p>2024-05-29T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1186/s12911-024-02540-0https://figshare.com/articles/journal_contribution/An_ensemble-based_machine_learning_model_for_predicting_type_2_diabetes_and_its_effect_on_bone_health/26404099CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/264040992024-05-29T09:00:00Z |
| spellingShingle | An ensemble-based machine learning model for predicting type 2 diabetes and its effect on bone health Belqes Alsadi (18695031) Biomedical and clinical sciences Clinical sciences Medical biochemistry and metabolomics Health sciences Health services and systems Information and computing sciences Machine learning Machine learning Diabetes Bone health Dual Energy X-ray Absorptiometry Qatar Biobank (QBB) |
| status_str | publishedVersion |
| title | An ensemble-based machine learning model for predicting type 2 diabetes and its effect on bone health |
| title_full | An ensemble-based machine learning model for predicting type 2 diabetes and its effect on bone health |
| title_fullStr | An ensemble-based machine learning model for predicting type 2 diabetes and its effect on bone health |
| title_full_unstemmed | An ensemble-based machine learning model for predicting type 2 diabetes and its effect on bone health |
| title_short | An ensemble-based machine learning model for predicting type 2 diabetes and its effect on bone health |
| title_sort | An ensemble-based machine learning model for predicting type 2 diabetes and its effect on bone health |
| topic | Biomedical and clinical sciences Clinical sciences Medical biochemistry and metabolomics Health sciences Health services and systems Information and computing sciences Machine learning Machine learning Diabetes Bone health Dual Energy X-ray Absorptiometry Qatar Biobank (QBB) |