Zahra Shiezadeh¹, Hedieh Sajedi² and Elham Aflakie³, ¹Islamic Azad University, Iran, ² University of Tehran, Iran and ³Shiraz University of Medical Sciences, Iran

Rheumatoid arthritis is one of the diseases that its cause is unknown yet; exploring the field of medical data mining can be helpful in early diagnosis and treatment of the disease. In this study, a predictive model is suggested that diagnoses rheumatoid arthritis. The rheumatoid arthritis dataset was collected from 2,564 patients referred to rheumatology clinic. For each patient a record consists of several clinical and demographic features is saved. After data analysis and pre-processing operations, three different methods are combined to choose proper features among all the features. Various data classification algorithms were applied on these features. Among these algorithms Adaboost had the highest precision. In this paper, we proposed a new classification algorithm entitled CS-Boost that employs Cuckoo search algorithm for optimizing the performance of Adaboost algorithm. Experimental results show that the CS-Boost algorithm enhance the accuracy of Adaboost in predicting of Rheumatoid Arthritis.

Data Mining, Adaboost, Cuckoo’s Algorithm, Predictive Model, Rheumatoid Arthritis, Decision Tree.