Implementation of the Rough Set Method With a Deep Learning Approach in the Process of Diagnosing Otitis Disease

Abstract

Otitis is a common health problem in the human ear and often requires a quick and accurate diagnosis. Deep learning is a method in the computer field to provide performance in classification. This research aims to develop an analysis model using a Deep Learning (DL) approach in diagnosing otitis. The method used in this development involves the performance of the Rough Set (RS) and Artificial Neural Network (ANN) methods to provide optimal analysis output. The research dataset refers to the clinical diagnosis of otitis patients which consists of 3 types, namely acute, effusion, and chronic. The test results of the analysis model developed using the DL approach were able to provide quite good output with an accuracy level of 99%. These results are based on the analysis pattern obtained based on the performance of the RS method. Based on these results, it can be concluded that the analytical model developed provides maximum and better results compared to the previous model based on the output and presentation of a systematic process in classifying otitis disease.