Development of machine learning models for detection of vision threatening Behçet’s disease (BD) using Egyptian College of Rheumatology (ECR)–BD cohort
Abstract Background Eye lesions, occur in nearly half of patients with Behçet’s Disease (BD), can lead to irreversible damage and vision loss; however, limited studies are available on identifying risk factors for the development of vision-threatening BD (VTBD). Using an Egyptian college of rheumato...
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BMC
2023-02-01
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| Series: | BMC Medical Informatics and Decision Making |
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| Online Access: | https://doi.org/10.1186/s12911-023-02130-6 |
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| author | Nevin Hammam Ali Bakhiet Eiman Abd El-Latif Iman I. El-Gazzar Nermeen Samy Rasha A. Abdel Noor Emad El-Shebeiny Amany R. El-Najjar Nahla N. Eesa Mohamed N. Salem Soha E. Ibrahim Dina F. El-Essawi Ahmed M. Elsaman Hanan M. Fathi Rehab A. Sallam Rawhya R. El Shereef Faten Ismail Mervat I. Abd-Elazeem Emtethal A. Said Noha M. Khalil Dina Shahin Hanan M. El-Saadany Marwa ElKhalifa Samah I. Nasef Ahmed M. Abdalla Nermeen Noshy Rasha M. Fawzy Ehab Saad Abdelhafeez Moshrif Amira T. El-Shanawany Yousra H. Abdel-Fattah Hossam M. Khalil Osman Hammam Aly Ahmed Fathy Tamer A. Gheita |
| author_facet | Nevin Hammam Ali Bakhiet Eiman Abd El-Latif Iman I. El-Gazzar Nermeen Samy Rasha A. Abdel Noor Emad El-Shebeiny Amany R. El-Najjar Nahla N. Eesa Mohamed N. Salem Soha E. Ibrahim Dina F. El-Essawi Ahmed M. Elsaman Hanan M. Fathi Rehab A. Sallam Rawhya R. El Shereef Faten Ismail Mervat I. Abd-Elazeem Emtethal A. Said Noha M. Khalil Dina Shahin Hanan M. El-Saadany Marwa ElKhalifa Samah I. Nasef Ahmed M. Abdalla Nermeen Noshy Rasha M. Fawzy Ehab Saad Abdelhafeez Moshrif Amira T. El-Shanawany Yousra H. Abdel-Fattah Hossam M. Khalil Osman Hammam Aly Ahmed Fathy Tamer A. Gheita |
| author_sort | Nevin Hammam |
| collection | DOAJ |
| description | Abstract Background Eye lesions, occur in nearly half of patients with Behçet’s Disease (BD), can lead to irreversible damage and vision loss; however, limited studies are available on identifying risk factors for the development of vision-threatening BD (VTBD). Using an Egyptian college of rheumatology (ECR)-BD, a national cohort of BD patients, we examined the performance of machine-learning (ML) models in predicting VTBD compared to logistic regression (LR) analysis. We identified the risk factors for the development of VTBD. Methods Patients with complete ocular data were included. VTBD was determined by the presence of any retinal disease, optic nerve involvement, or occurrence of blindness. Various ML-models were developed and examined for VTBD prediction. The Shapley additive explanation value was used for the interpretability of the predictors. Results A total of 1094 BD patients [71.5% were men, mean ± SD age 36.1 ± 10 years] were included. 549 (50.2%) individuals had VTBD. Extreme Gradient Boosting was the best-performing ML model (AUROC 0.85, 95% CI 0.81, 0.90) compared with logistic regression (AUROC 0.64, 95%CI 0.58, 0.71). Higher disease activity, thrombocytosis, ever smoking, and daily steroid dose were the top factors associated with VTBD. Conclusions Using information obtained in the clinical settings, the Extreme Gradient Boosting identified patients at higher risk of VTBD better than the conventional statistical method. Further longitudinal studies to evaluate the clinical utility of the proposed prediction model are needed. |
| first_indexed | 2024-04-09T22:53:15Z |
| format | Article |
| id | doaj.art-c8b6a4fddf254e65a950655b8b558f4f |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-04-09T22:53:15Z |
| publishDate | 2023-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-c8b6a4fddf254e65a950655b8b558f4f2023-03-22T11:31:36ZengBMCBMC Medical Informatics and Decision Making1472-69472023-02-0123111310.1186/s12911-023-02130-6Development of machine learning models for detection of vision threatening Behçet’s disease (BD) using Egyptian College of Rheumatology (ECR)–BD cohortNevin Hammam0Ali Bakhiet1Eiman Abd El-Latif2Iman I. El-Gazzar3Nermeen Samy4Rasha A. Abdel Noor5Emad El-Shebeiny6Amany R. El-Najjar7Nahla N. Eesa8Mohamed N. Salem9Soha E. Ibrahim10Dina F. El-Essawi11Ahmed M. Elsaman12Hanan M. Fathi13Rehab A. Sallam14Rawhya R. El Shereef15Faten Ismail16Mervat I. Abd-Elazeem17Emtethal A. Said18Noha M. Khalil19Dina Shahin20Hanan M. El-Saadany21Marwa ElKhalifa22Samah I. Nasef23Ahmed M. Abdalla24Nermeen Noshy25Rasha M. Fawzy26Ehab Saad27Abdelhafeez Moshrif28Amira T. El-Shanawany29Yousra H. Abdel-Fattah30Hossam M. Khalil31Osman Hammam32Aly Ahmed Fathy33Tamer A. Gheita34Department of Rheumatology and Rehabilitation, Faculty of Medicine, Assiut UniversityComputer Science Department, Higher Institute of Computer Science and Information Systems, Culture and Science CityOphthalmology Department, Faculty of Medicine, Alexandria UniversityRheumatology Department, Faculty of Medicine, Cairo UniversityRheumatology Unit, Internal Medicine Department, Faculty of Medicine, Ain-Shams UniversityRheumatology Unit, Internal Medicine Department, Tanta UniversityRheumatology Unit, Internal Medicine Department, Menoufia UniversityRheumatology Department, Faculty of Medicine, Zagazig UniversityRheumatology Department, Faculty of Medicine, Cairo UniversityRheumatology Unit, Internal Medicine Department, Faculty of Medicine, Beni-Suef UniversityRheumatology Department, Faculty of Medicine, Ain Shams UniversityInternal Medicine Department, Rheumatology and Rehabilitation Clinic, National Centre for Radiation Research and Technology, Egyptian Atomic Energy Authority (AEA)Rheumatology Department, Faculty of Medicine, Sohag UniversityRheumatology Department, Faculty of Medicine, Fayoum UniversityRheumatology Department, Faculty of Medicine, Mansoura UniversityRheumatology Department, Faculty of Medicine, Minia UniversityRheumatology Department, Faculty of Medicine, Minia UniversityRheumatology Department, Faculty of Medicine, Beni-Suef UniversityRheumatology Department, Faculty of Medicine, Benha UniversityRheumatology Unit, Internal Medicine Department, Faculty of Medicine, Cairo UniversityRheumatology Unit, Internal Medicine Department, Faculty of Medicine, Mansoura UniversityRheumatology Department, Faculty of Medicine, Tanta UniversityRheumatology Unit, Internal Medicine Department, Faculty of Medicine, Alexandria UniversityRheumatology and Rehabilitation Department, Faculty of Medicine, Suez-Canal UniversityRheumatology Department, Faculty of Medicine, Aswan UniversityRheumatology Department, Faculty of Medicine, Benha UniversityRheumatology Department, Faculty of Medicine, Benha UniversityRheumatology Department, Faculty of Medicine, South Valley UniversityRheumatology Department, Faculty of Medicine, Al-Azhar UniversityRheumatology Department, Faculty of Medicine, Menoufia UniversityRheumatology Department, Faculty of Medicine, Alexandria UniversityOphthalmology Department, Faculty of Medicine, Beni-Suef UniversityDepartment of Rheumatology and Rehabilitation, Faculty of Medicine, New Valley UniversityOphthalmology Department, Faculty of Medicine, Al-Azhar Assiut UniversityRheumatology Department, Kasr Al Ainy School of Medicine, Cairo UniversityAbstract Background Eye lesions, occur in nearly half of patients with Behçet’s Disease (BD), can lead to irreversible damage and vision loss; however, limited studies are available on identifying risk factors for the development of vision-threatening BD (VTBD). Using an Egyptian college of rheumatology (ECR)-BD, a national cohort of BD patients, we examined the performance of machine-learning (ML) models in predicting VTBD compared to logistic regression (LR) analysis. We identified the risk factors for the development of VTBD. Methods Patients with complete ocular data were included. VTBD was determined by the presence of any retinal disease, optic nerve involvement, or occurrence of blindness. Various ML-models were developed and examined for VTBD prediction. The Shapley additive explanation value was used for the interpretability of the predictors. Results A total of 1094 BD patients [71.5% were men, mean ± SD age 36.1 ± 10 years] were included. 549 (50.2%) individuals had VTBD. Extreme Gradient Boosting was the best-performing ML model (AUROC 0.85, 95% CI 0.81, 0.90) compared with logistic regression (AUROC 0.64, 95%CI 0.58, 0.71). Higher disease activity, thrombocytosis, ever smoking, and daily steroid dose were the top factors associated with VTBD. Conclusions Using information obtained in the clinical settings, the Extreme Gradient Boosting identified patients at higher risk of VTBD better than the conventional statistical method. Further longitudinal studies to evaluate the clinical utility of the proposed prediction model are needed.https://doi.org/10.1186/s12911-023-02130-6Behçet’s diseaseVision-threatening BDMachine learningSHAP analysis |
| spellingShingle | Nevin Hammam Ali Bakhiet Eiman Abd El-Latif Iman I. El-Gazzar Nermeen Samy Rasha A. Abdel Noor Emad El-Shebeiny Amany R. El-Najjar Nahla N. Eesa Mohamed N. Salem Soha E. Ibrahim Dina F. El-Essawi Ahmed M. Elsaman Hanan M. Fathi Rehab A. Sallam Rawhya R. El Shereef Faten Ismail Mervat I. Abd-Elazeem Emtethal A. Said Noha M. Khalil Dina Shahin Hanan M. El-Saadany Marwa ElKhalifa Samah I. Nasef Ahmed M. Abdalla Nermeen Noshy Rasha M. Fawzy Ehab Saad Abdelhafeez Moshrif Amira T. El-Shanawany Yousra H. Abdel-Fattah Hossam M. Khalil Osman Hammam Aly Ahmed Fathy Tamer A. Gheita Development of machine learning models for detection of vision threatening Behçet’s disease (BD) using Egyptian College of Rheumatology (ECR)–BD cohort BMC Medical Informatics and Decision Making Behçet’s disease Vision-threatening BD Machine learning SHAP analysis |
| title | Development of machine learning models for detection of vision threatening Behçet’s disease (BD) using Egyptian College of Rheumatology (ECR)–BD cohort |
| title_full | Development of machine learning models for detection of vision threatening Behçet’s disease (BD) using Egyptian College of Rheumatology (ECR)–BD cohort |
| title_fullStr | Development of machine learning models for detection of vision threatening Behçet’s disease (BD) using Egyptian College of Rheumatology (ECR)–BD cohort |
| title_full_unstemmed | Development of machine learning models for detection of vision threatening Behçet’s disease (BD) using Egyptian College of Rheumatology (ECR)–BD cohort |
| title_short | Development of machine learning models for detection of vision threatening Behçet’s disease (BD) using Egyptian College of Rheumatology (ECR)–BD cohort |
| title_sort | development of machine learning models for detection of vision threatening behcet s disease bd using egyptian college of rheumatology ecr bd cohort |
| topic | Behçet’s disease Vision-threatening BD Machine learning SHAP analysis |
| url | https://doi.org/10.1186/s12911-023-02130-6 |
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