A machine learning based study to assess bone health in a diabetic cohort
<p dir="ltr">Diabetes mellitus (DM) and osteoporosis/osteopenia affect millions of people globally and are major health conditions in several countries including Qatar. <u>Bone mineral density </u>(BMD) is a widely accepted indicator for diagnosing osteoporosis (OP) and o...
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2022
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| _version_ | 1864513547724128256 |
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| author | Saleh Musleh (15279190) |
| author2 | Anjanarani Nazeemudeen (17058015) Mohammad Tariqul Islam (7854059) Nady El Hajj (686554) Tanvir Alam (638619) |
| author2_role | author author author author |
| author_facet | Saleh Musleh (15279190) Anjanarani Nazeemudeen (17058015) Mohammad Tariqul Islam (7854059) Nady El Hajj (686554) Tanvir Alam (638619) |
| author_role | author |
| dc.creator.none.fl_str_mv | Saleh Musleh (15279190) Anjanarani Nazeemudeen (17058015) Mohammad Tariqul Islam (7854059) Nady El Hajj (686554) Tanvir Alam (638619) |
| dc.date.none.fl_str_mv | 2022-09-13T15:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.imu.2022.101079 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_machine_learning_based_study_to_assess_bone_health_in_a_diabetic_cohort/29116916 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Health sciences Health services and systems Information and computing sciences Machine learning Diabetes Osteoporosis Osteopenia Bone mineral density Dual energy X-ray absorptiometry DXA Machine learning Qatar Biobank (QBB) |
| dc.title.none.fl_str_mv | A machine learning based study to assess bone health in a diabetic cohort |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Diabetes mellitus (DM) and osteoporosis/osteopenia affect millions of people globally and are major health conditions in several countries including Qatar. <u>Bone mineral density </u>(BMD) is a widely accepted indicator for diagnosing osteoporosis (OP) and osteopenia (OPN). The best method for determining bone mineral density and OP/OPN risk is via dual energy X-ray absorptiometry (DXA) technology. The risk of osteoporosis-related fracture may increase for people with diabetes. Therefore, it is necessary to develop a system that can support the early detection of OP/OPN in diabetic patients. In this study, we analyzed Qatar diabetic cohorts including 500 subjects, among which 68 were OP/OPN (target) subjects and 432 were without osteoporosis/osteopenia (control) subjects. The objective of this study is to develop an ML model to distinguish diabetic OP/OPN patients from diabetic non-OP/non-OPN subjects based on their bone health indicators from full body DXA scan measurements. Based on our experiments, AdaBoost model performed the best for classifying the target group from the control group. 10-fold cross validation-based results indicate that the proposed ML model was able to distinguish the target group from the control group at 80% sensitivity, 96% specificity. To the best of our knowledge, our study is the first ML-based approach to detect the early onset of OP/OPN in diabetic cohort from Qatar. Our analyses revealed the higher level of lean mass, <u>fat mass</u> and bone mass for the control group compared to the target group. Higher levels of <u>BMC</u>, BMD from different body parts in the control group compared to the osteoporosis/osteopenia group indicate the protective effects of obesity on bone health in the Qatari diabetic cohort. Moreover, higher value of anthropometric measurements in troch, lumbar spine (L1, L2, L3, L4), pelvis and other body parts in the control group indicates that the WHO guideline can be applied to the Qatari diabetic cohort for the early detection of OP/OPN based on the proposed ML model. Further research on OP/OPN in diabetic patients is warranted in future to confirm the role of DM on bone health.</p><h2>Other Information</h2><p dir="ltr">Published in: Informatics in Medicine Unlocked<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.imu.2022.101079" target="_blank">https://dx.doi.org/10.1016/j.imu.2022.101079</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_8e7224833f22c33ad132b9d1969692d2 |
| identifier_str_mv | 10.1016/j.imu.2022.101079 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29116916 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A machine learning based study to assess bone health in a diabetic cohortSaleh Musleh (15279190)Anjanarani Nazeemudeen (17058015)Mohammad Tariqul Islam (7854059)Nady El Hajj (686554)Tanvir Alam (638619)Health sciencesHealth services and systemsInformation and computing sciencesMachine learningDiabetesOsteoporosisOsteopeniaBone mineral densityDual energy X-ray absorptiometryDXAMachine learningQatar Biobank (QBB)<p dir="ltr">Diabetes mellitus (DM) and osteoporosis/osteopenia affect millions of people globally and are major health conditions in several countries including Qatar. <u>Bone mineral density </u>(BMD) is a widely accepted indicator for diagnosing osteoporosis (OP) and osteopenia (OPN). The best method for determining bone mineral density and OP/OPN risk is via dual energy X-ray absorptiometry (DXA) technology. The risk of osteoporosis-related fracture may increase for people with diabetes. Therefore, it is necessary to develop a system that can support the early detection of OP/OPN in diabetic patients. In this study, we analyzed Qatar diabetic cohorts including 500 subjects, among which 68 were OP/OPN (target) subjects and 432 were without osteoporosis/osteopenia (control) subjects. The objective of this study is to develop an ML model to distinguish diabetic OP/OPN patients from diabetic non-OP/non-OPN subjects based on their bone health indicators from full body DXA scan measurements. Based on our experiments, AdaBoost model performed the best for classifying the target group from the control group. 10-fold cross validation-based results indicate that the proposed ML model was able to distinguish the target group from the control group at 80% sensitivity, 96% specificity. To the best of our knowledge, our study is the first ML-based approach to detect the early onset of OP/OPN in diabetic cohort from Qatar. Our analyses revealed the higher level of lean mass, <u>fat mass</u> and bone mass for the control group compared to the target group. Higher levels of <u>BMC</u>, BMD from different body parts in the control group compared to the osteoporosis/osteopenia group indicate the protective effects of obesity on bone health in the Qatari diabetic cohort. Moreover, higher value of anthropometric measurements in troch, lumbar spine (L1, L2, L3, L4), pelvis and other body parts in the control group indicates that the WHO guideline can be applied to the Qatari diabetic cohort for the early detection of OP/OPN based on the proposed ML model. Further research on OP/OPN in diabetic patients is warranted in future to confirm the role of DM on bone health.</p><h2>Other Information</h2><p dir="ltr">Published in: Informatics in Medicine Unlocked<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.imu.2022.101079" target="_blank">https://dx.doi.org/10.1016/j.imu.2022.101079</a></p>2022-09-13T15:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.imu.2022.101079https://figshare.com/articles/journal_contribution/A_machine_learning_based_study_to_assess_bone_health_in_a_diabetic_cohort/29116916CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/291169162022-09-13T15:00:00Z |
| spellingShingle | A machine learning based study to assess bone health in a diabetic cohort Saleh Musleh (15279190) Health sciences Health services and systems Information and computing sciences Machine learning Diabetes Osteoporosis Osteopenia Bone mineral density Dual energy X-ray absorptiometry DXA Machine learning Qatar Biobank (QBB) |
| status_str | publishedVersion |
| title | A machine learning based study to assess bone health in a diabetic cohort |
| title_full | A machine learning based study to assess bone health in a diabetic cohort |
| title_fullStr | A machine learning based study to assess bone health in a diabetic cohort |
| title_full_unstemmed | A machine learning based study to assess bone health in a diabetic cohort |
| title_short | A machine learning based study to assess bone health in a diabetic cohort |
| title_sort | A machine learning based study to assess bone health in a diabetic cohort |
| topic | Health sciences Health services and systems Information and computing sciences Machine learning Diabetes Osteoporosis Osteopenia Bone mineral density Dual energy X-ray absorptiometry DXA Machine learning Qatar Biobank (QBB) |