While the success rate of the model was 91.25 % with only blood count data, the success rate was 97.90% with the addition of the patient’s history. In the study of machine learning techniques, the highest success rate was achieved with the LR model.
The performed DSS has achieved a diagnostic success rate of 94.05% on 286 patients. The results of the fuzzy-based DSS were also compared with the diagnoses made by the specialist and the diagnoses made according to the 2010 ACR / EULAR RA classification criteria. To examine the effect of the patient’s history on RA disease diagnosis, two different models were used in machine learning techniques, one with and one without the patient’s history. In the second method, RA disease was diagnosed using kNN, SVM, LR, DT, NB, and MLP algorithms, which are widely used in machine learning. The DSS is web-based and publicly accessible. The FES result that determines RA disease severity, the Anti-CCP level that is used to distinguish RA disease, and the patient’s medical history were used to design the Decision Support System (DSS) that diagnoses RA disease. In the first method, an FES structure that determines the severity of RA disease has been established from blood count using the laboratory test results of CRP, ESR, RF, and ANA. For this purpose, blood counts were taken from 286 people, including 91 men and 195 women from various age groups. In this study, the diagnosis of RA was made with two different methods using a fuzzy expert system (FES) and machine learning (ML) techniques, which were designed and implemented with the help of a specialist in the field, and the results were compared. The diagnosis of RA usually requires a physical examination, laboratory tests, and a review of the patient’s medical history. Early diagnosis of RA and knowing the severity of the disease are very important for the treatment to be applied.
Rheumatoid Arthritis (RA) is a very common autoimmune disease that causes significant morbidity and mortality, and therefore early diagnosis and treatment are important.
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