Restricted boltzmann machine and softmax regression for acute respiratory infections disease identification
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Abstract
Restricted boltzmann machines (RBM) have attracted much attention lately after being proposed as building blocks of deep learning blocks. RBM is an algorithm that belongs to the artificial neural network (ANN) algorithm. Deep learning models can be used in the health field to identify diseases using medical data records. Acute Respiratory Infection (ARI) is a disease that infects the respiratory tract. A patient infected by ARI diseases is high. To identify ARI can use the symptoms that the patient had experienced. Based on this background, this study aims to help identify ARI disease using its symptoms. The method used for identification is the deep learning model, which was built using the RBM and softmax regression. Three steps were used in this research, which are training, testing, and implementation. The trained deep learning model will be implemented to identify ARI disease. This research will use ARI data from Puskemas Warungasem, Indonesia. From the research result, the deep learning model can get an accuracy of 96%. The deep learning configuration used in this research has 4 RBM layers, 1 Softmax layer as the output layer, and a learning rate value of 0.01 and 1000 iterations. This research can be used as a reference so that the next researcher can add other algorithms to Deep learning to improve accuracy.
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