Diagnosis Osteoporosis Risk: Using Machine Learning Algorithms Among Fasa Adults Cohort Study (FACS)
ABSTRACT Introduction In Iran, the assessment of osteoporosis through tools like dual‐energy X‐ray absorptiometry poses significant challenges due to their high costs and limited availability, particularly in small cities and rural areas. Our objective was to employ a variety of machine learning (ML...
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Endocrinology, Diabetes & Metabolism |
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| Online Access: | https://doi.org/10.1002/edm2.70023 |
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| author | Saghar Tabib Seyed Danial Alizadeh Aref Andishgar Babak Pezeshki Omid Keshavarzian Reza Tabrizi |
| author_facet | Saghar Tabib Seyed Danial Alizadeh Aref Andishgar Babak Pezeshki Omid Keshavarzian Reza Tabrizi |
| author_sort | Saghar Tabib |
| collection | DOAJ |
| description | ABSTRACT Introduction In Iran, the assessment of osteoporosis through tools like dual‐energy X‐ray absorptiometry poses significant challenges due to their high costs and limited availability, particularly in small cities and rural areas. Our objective was to employ a variety of machine learning (ML) techniques to evaluate the accuracy and precision of each method, with the aim of identifying the most accurate pattern for diagnosing the osteoporosis risks. Methods We analysed the data related to osteoporosis risk factors obtained from the Fasa Adults Cohort Study in eight ML methods, including logistic regression (LR), baseline LR, decision tree classifiers (DT), support vector classifiers (SVC), random forest classifiers (RF), linear discriminant analysis (LDA), K nearest neighbour classifiers (KNN) and extreme gradient boosting (XGB). For each algorithm, we calculated accuracy, precision, sensitivity, specificity, F1 score and area under the curve (AUC) and compared them. Results The XGB model with an AUC of 0.78 (95% confidence interval [CI]: 0.74–0.82) and an accuracy of 0.79 (0.75–0.83) demonstrated the best performance, while AUC and accuracy values of RF were achieved (0.78 and 0.77), LR (0.78 and 0.77), LDA (0.78 and 0.76), DT (0.76 and 0.79), SVC (0.71 and 0.64), KNN (0.63 and 0.59) and baseline LR (0.72 and 0.82), respectively. Conclusion The XGB model had the best performance in assessing the risk of osteoporosis in the Iranian population. Given the disadvantages and challenges associated with traditional osteoporosis diagnostic tools, the implementation of ML algorithms for the early identification of individuals with osteoporosis can lead to a significant reduction in morbidity and mortality related to this condition. This advancement not only alleviates the substantial financial burden placed on the healthcare systems of various countries due to the treatment of complications arising from osteoporosis but also encourages health policies to shift toward more preventive approaches for managing this disease. |
| format | Article |
| id | doaj-art-eee629360e2945babf56f02e491a93d5 |
| institution | Kabale University |
| issn | 2398-9238 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Endocrinology, Diabetes & Metabolism |
| spelling | doaj-art-eee629360e2945babf56f02e491a93d52025-01-25T18:20:28ZengWileyEndocrinology, Diabetes & Metabolism2398-92382025-01-0181n/an/a10.1002/edm2.70023Diagnosis Osteoporosis Risk: Using Machine Learning Algorithms Among Fasa Adults Cohort Study (FACS)Saghar Tabib0Seyed Danial Alizadeh1Aref Andishgar2Babak Pezeshki3Omid Keshavarzian4Reza Tabrizi5Student Research Committee, School of Medicine Fasa University of Medical Sciences Fasa IranSina Trauma and Surgery Research Centre Tehran University of Medical Sciences Tehran IranStudent Research Committee, School of Medicine Fasa University of Medical Sciences Fasa IranClinical Research Development Unit, Valiasr Hospital Fasa University of Medical Sciences Fasa IranSchool of Medicine Shiraz University of Medical Sciences Shiraz IranNoncommunicable Diseases Research Center Fasa University of Medical Sciences Fasa IranABSTRACT Introduction In Iran, the assessment of osteoporosis through tools like dual‐energy X‐ray absorptiometry poses significant challenges due to their high costs and limited availability, particularly in small cities and rural areas. Our objective was to employ a variety of machine learning (ML) techniques to evaluate the accuracy and precision of each method, with the aim of identifying the most accurate pattern for diagnosing the osteoporosis risks. Methods We analysed the data related to osteoporosis risk factors obtained from the Fasa Adults Cohort Study in eight ML methods, including logistic regression (LR), baseline LR, decision tree classifiers (DT), support vector classifiers (SVC), random forest classifiers (RF), linear discriminant analysis (LDA), K nearest neighbour classifiers (KNN) and extreme gradient boosting (XGB). For each algorithm, we calculated accuracy, precision, sensitivity, specificity, F1 score and area under the curve (AUC) and compared them. Results The XGB model with an AUC of 0.78 (95% confidence interval [CI]: 0.74–0.82) and an accuracy of 0.79 (0.75–0.83) demonstrated the best performance, while AUC and accuracy values of RF were achieved (0.78 and 0.77), LR (0.78 and 0.77), LDA (0.78 and 0.76), DT (0.76 and 0.79), SVC (0.71 and 0.64), KNN (0.63 and 0.59) and baseline LR (0.72 and 0.82), respectively. Conclusion The XGB model had the best performance in assessing the risk of osteoporosis in the Iranian population. Given the disadvantages and challenges associated with traditional osteoporosis diagnostic tools, the implementation of ML algorithms for the early identification of individuals with osteoporosis can lead to a significant reduction in morbidity and mortality related to this condition. This advancement not only alleviates the substantial financial burden placed on the healthcare systems of various countries due to the treatment of complications arising from osteoporosis but also encourages health policies to shift toward more preventive approaches for managing this disease.https://doi.org/10.1002/edm2.70023diagnosisFasa Adult Cohort Studymachine learningosteoporosis |
| spellingShingle | Saghar Tabib Seyed Danial Alizadeh Aref Andishgar Babak Pezeshki Omid Keshavarzian Reza Tabrizi Diagnosis Osteoporosis Risk: Using Machine Learning Algorithms Among Fasa Adults Cohort Study (FACS) Endocrinology, Diabetes & Metabolism diagnosis Fasa Adult Cohort Study machine learning osteoporosis |
| title | Diagnosis Osteoporosis Risk: Using Machine Learning Algorithms Among Fasa Adults Cohort Study (FACS) |
| title_full | Diagnosis Osteoporosis Risk: Using Machine Learning Algorithms Among Fasa Adults Cohort Study (FACS) |
| title_fullStr | Diagnosis Osteoporosis Risk: Using Machine Learning Algorithms Among Fasa Adults Cohort Study (FACS) |
| title_full_unstemmed | Diagnosis Osteoporosis Risk: Using Machine Learning Algorithms Among Fasa Adults Cohort Study (FACS) |
| title_short | Diagnosis Osteoporosis Risk: Using Machine Learning Algorithms Among Fasa Adults Cohort Study (FACS) |
| title_sort | diagnosis osteoporosis risk using machine learning algorithms among fasa adults cohort study facs |
| topic | diagnosis Fasa Adult Cohort Study machine learning osteoporosis |
| url | https://doi.org/10.1002/edm2.70023 |
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