Behçet hastalığının erken teşhisi için semptom temelli bir karar destek aracı

Behcet's disease (BD) is an inflammatory disorder that belongs to the systemic vasculitic syndromes and affects all types of vessels and sizes. BD in Turkey has the highest incidence among other countries (between 20 upto 420 per 100,000 people). Since no definitive test is available for BD, general basis for detection of this disorder is looking for physical manifestations. As time progresses the symptoms get worse, Therefore, early detection of the disease is important and could make patients' condition less painful. Nowadays, several studies with focus on developing clinical decision support system for early diagnosis of various diseases have been performed. In this thesis, we intended to work on a pre-diagnosis method of Behcet's disease and develop a mathematical model based on Bayes' theorem and conditional probability for patients, who are suspicious of BD. By performing the model in MATLAB program, we created a clinical decision support system that is able to detect BD cases based on physical symptoms. Our proposed system was able to detect BD cases with high frequency and also could exclude cases with diseases other than BD. We believe, this model could provide support for people in early diagnoses of Behcet's disease and help refer them to the proper expert