[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122601-en":3,"doc-seo-122601-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122601,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",7,"Healthcare","Machine Learning Approach for Predicting Systemic Lupus Erythematosus in Oman-based Cohort","Designing an explainable machine learning prediction framework for Systemic Lupus Erythematosus (SLE) in an Oman patient cohort. Using electronic hospital records for 219 patients (2006–2019), the study distinguishes 138 SLE cases from 81 other rheumatologic diseases, emphasizing early-stage clinical and demographic signals. Recursive Feature Selection reduces inputs to the most informative variables, while CatBoost predicts SLE and SHAP provides individual, validated reasoning. CatBoost reaches an ROC AUC of 0.95 with 92% sensitivity, highlighting alopecia, renal disorders, acute cutaneous lupus, hemolytic anemia, and age as top contributors.","1 SUBMITTED 23 JUN 22  \n2 REVISIONS REQ. 18 SEPT & 14 NOV 22; REVISIONS RECD. 23 OCT & 24 NOV 22  \n3 ACCEPTED 8 DEC 22  \n4 ONLINE-FIRST: DECEMBER 2022  \n5 DOI: [https://doi.org/10.18295/squmj.12.2022.069](https://doi.org/10.18295/squmj.12.2022.069)[ ](https://doi.org/10.18295/squmj.12.2022.069)6  \n7 Machine Learning Approach for Predicting Systemic Lupus Erythematosus  \n8 in Oman-based Cohort  \n9 *AlHassan AlShareedah,1 Hamza Zidoum,1 Sumaya Al-Sawafi,1 Batool Al- 10 Lawati,2 Aliya Al-Ansari3  \n11  \n12 Departments of 1 Computer Science and 3Biology, College of Science and 2Department of  \n13 Medicine, College of Medicine, Sultan Qaboos University, Muscat, Oman.  \n14 *Corresponding Author’s e-mail: [al.hassan.satii@gmail.com](al.hassan.satii@gmail.com)  \n15  \n16 Abstract  \n17 Objectives: Design a machine learning-based prediction framework to predict the presence or  \n18 absence of Systemic Lupus Erythematosus (SLE) in a cohort of Omani patients. Methods: Records  \n19 of 219 patients from 2006 to 2019 were extracted from SQU Hospital electronic records, 138  \n20 patients have SLE, and the remaining 81 have other rheumatologic diseases. Clinical and  \n21 demographic features were analyzed to focus on the early stages of the disease. Our design  \n22 implements Recursive Feature Selection (RFE) to select only the most informative features. In  \n23 addition, the CatBoost classification algorithm is utilized to predict SLE and an explainer algorithm  \n24 (SHAP) is applied on top of the CatBoost model to provide individual prediction reasoning which is  \n25 then validated by rheumatologists. Results: CatBoost achieved an Area Under the ROC curve  \n26 (AUC) score of 0.95 and a Sensitivity of 92% . Four clinical features (Alopecia, renal disorders, 27 Acute Cutaneous Lupus, and hemolytic anemia) along with the patient’s age were shown to have  \n28 the greatest contribution to the prediction by the SHAP algorithm. Conclusion: We have designed  \n29 and validated an explainable framework to predict SLE patients and provide reasoning for its  \n30 prediction. Our framework enables early intervention for clinicians which leads to positive  \n31 healthcare outcomes.  \n32 Keywords: Systemic Lupus Erythematosus; Interpretation; Machine Learning; Supervised;  \n33 Clinical Decision Support System; Statistical Data; Data Analysis.  \n34  \n35 Advances in Knowledge  \n36 􀁸 The first self-explainable prediction framework for SLE disease specific to the Omani  \n37 population is developed.  \n38 􀁸 Achieved an AUC score of 0.956 and Sensitivity of 92% .  \n39 􀁸 Identifies patterns in clinical manifestation which are unique to the Omani population.  \n40 􀁸 The patient’s age and four clinical features (renal disorders, alopecia, cutaneous lupus, and 41 hemolytic anemia) had the highest contribution to the model’s prediction.  \n42 􀁸 Compared to other Arab ethnicities, renal disorders frequency in Oman was the highest  \n43 while alopecia frequency was the lowest.  \n44 􀁸  \n45 Application to Patient Care  \n46 􀁸 The model can potentially be used as a clinical decision support system that alerts clinicians  \n47 to the presence of SLE which prompts further investigation until an official diagnosis is  \n48 made.  \n49 􀁸 Enabling clinicians to contrast the information reported by the model with their knowledge  \n50 through an interpretation algorithm. Thereby increasing the probability of correct diagnosis  \n51 and encouraging the adoption of Machine Learning (ML) in healthcare.  \n52 􀁸 A practical introduction of machine learning and interpretation tools to the medical  \n53 diagnosing process that improves early detection of SLE; a crucial factor in lowering flare  \n54 rate and reducing mortality.  \n55  \n56 Introduction  \n57 Systemic lupus erythematosus (SLE) is a chronic multisystem autoimmune disease. SLE is  \n58 caused by genetic and environmental factors that potentiate the creation of high-titer  \n59 autoantibodies aimed at native DNA and other cellular elements.1 T","cbCaiiRP5RCmBNsK","https://ap.wps.com/l/cbCaiiRP5RCmBNsK","pdf",1065731,1,19,"English","en",105,"# Abstract\n## Objectives\n## Methods\n## Results\n## Conclusion\n# Advances in Knowledge\n# Application to Patient Care\n# Introduction","[{\"question\":\"What is the study’s main objective?\",\"answer\":\"To design a machine learning-based prediction framework that identifies the presence or absence of SLE in an Oman-based patient cohort.\"},{\"question\":\"How were the prediction features selected and interpreted?\",\"answer\":\"Recursive Feature Selection (RFE) selects the most informative features, and SHAP is applied on top of the CatBoost model to provide individual prediction explanations.\"},{\"question\":\"Which clinical features contributed most to SLE prediction?\",\"answer\":\"SHAP indicates the greatest contributions come from alopecia, renal disorders, acute cutaneous lupus, and hemolytic anemia, together with the patient’s age.\"}]","Machine Learning Approach for Predicting Systemic Lupus Erythematosus in Oman-based Cohort | 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