Tuberculosis Disease Diagnosis Using Artificial Neural Networks

Abstract

Tuberculosis is an infectious disease, caused in most cases by microorganisms called Mycobacterium tuberculosis. Tuberculosis is a great problem in most low income countries; it is the single most frequent cause of death in individuals aged fifteen to forty-nine years. Tuberculosis is important health problem in Turkey also. In this study, a study on tuberculosis diagnosis was realized by using multilayer neural networks (MLNN). For this purpose, two different MLNN structures were used. One of the structures was the MLNN with one hidden layer and the other was the MLNN with two hidden layers. A general regression neural network (GRNN) was also performed to realize tuberculosis diagnosis for the comparison. Levenberg-Marquardt algorithms were used for the training of the multilayer neural networks. The results of the study were compared with the results of the pervious similar studies reported focusing on tuberculosis diseases diagnosis. The tuberculosis dataset were taken from a state hospital's database using patient's epicrisis reports.

Correlation between drinking water fluoride and TSH hormone by ANNs and ANFIS

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Estimating the Statistical Significance of Classifiers used in the Prediction of Tuberculosis

“…and Temuritus [1] present a study on tuberculosis diagnosis, carried out with the help of MultiLayer Neural Networks (MLNNs). For this purpose, an MLNN with two hidden layers and a genetic algorithm for training algorithm has been used.…”

An Artificial Neural Network Design for Determination of Hashimoto’s Thyroiditis Sub-Groups

“…In Caturegli, Remigis, and Rose (2014), determination of thyroid illnesses was carried out via ANN. In Er, Temurtas, and Tanrıkulu (2008), diagnosis of tuberculosis was performed by ANN with 95.08% accuracy. Castanho, Hernandes, De Re, Rautenberg, and Billis (2013) used an expert system for predicting the pathological stage of prostate cancer.…”