APPLICATION OF DEEP BOLTZMANN MACHINE IN DIAGNOSIS PROCESSES OF HEPATITIS TYPES B & C

Abstract

Correct diagnosis of diseases is the main problem in medicine. Artificial intelligence and learning methods have been developed to solve problems in many fields, such as biology and medical sciences. Correct diagnosis before treatment is the most challenging and the first step in achieving proper cures. The primary objective of this paper is to introduce an intelligent system that develops beyond the deep neural network. It can diagnose and distinguish between Hepatitis types B and C by using a set of general tests for liver health. The deep network used in this research is the Deep Boltzmann Machine (DBM). Learning components within Restricted Boltzmann Machine (RBM) lead to intended results. The RBMs extract features to be used in an efficient classification process. An RBM is robust computing and well-suited to extract high-level features and diagnose hepatitis B and C. The method was tested on general items in laboratory tests that check the liver’s health. The DBM could predict hepatitis type B and C with an accuracy between 90.1% and 92.04%. Predictive accuracy was obtained with10-fold cross-validation. Compared with other methods, simulation results on DBM architecture reveal the proposed method’s efficiency in diagnosing Hepatitis B and C. What made this approach successful was a deep learning network in addition to discovering communication and extracting knowledge from the data.