Improve data classification performance in diagnosing diabetes using the Binary Exchange Market Algorithm

Abstract

Today's lifestyle has led to a significant increase in referrals to medical centers to diagnose various diseases. To this end, over the past few years, researchers have turned to new diagnostic methods, including data mining and artificial intelligence, intending to facilitate the detection process and increase reliability. The high volume of data available in medical centers can be considered one of the main problems in using these methods. The optimal selection of essential and influential features reduces the maximum dimension for better diagnosis with more reliability of results. In this paper, a new approach uses a Binary Exchange Market Algorithm (BEMA) to identify essential and practical features in the diabetes dataset and determine the best algorithm binary function (type of sigmoid function) to improve the performance of the EMA algorithm is presented. For validation and efficiency of the proposed BEMA algorithm, several SVM, KNN, and NB classification models have been used to train and test the final model. The results obtained from the evaluations show that the proposed BEMA-SVM combined method has a better performance than the previous methods to improve accuracy and offer an effect equivalent to 98.502%. Also, to provide better results and more reliability than the proposed method, researchers can use a combination of several classes with the proposed method, which is outside the scope of this study.