Enhancing the Diagnosis of Behcet's Disease Using Machine Learning: A Comparative Study on Clinical Data From Saudi Arabia.

ABSTRACT: Behçet's disease (BD) is one of the most difficult diseases to diagnose in the field of rheumatic immune diseases because it is rare, has many different symptoms, and we do not know much about how it works. Instead of trying to make a direct clinical diagnosis, this study was set up as an exploratory investigation to find out more about BD and figure out which clinical and laboratory features are most important. To accomplish this, clinical data were gathered from 148 patients (76 with bipolar disorder and 72 with rheumatoid arthritis) at the Rheumatology Clinic of King Abdulaziz University. We used several machine learning (ML) algorithms, such as decision tree, bagging, random forest (RF), XGBoost, and support vector machines (SVMs), to see if they could learn patterns that set BD apart

SUBMITTER: Alalwany H 

PROVIDER: S-EPMC12831129 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature