Feyzullah Temurtas

A comparative study on diabetes disease diagnosis using neural networks

Diabetes occurs when a body is unable to produce or respond properly to insulin which is needed to regulate glucose. Besides contributing to heart disease, diabetes also increases the risks of developing kidney disease, blindness, nerve damage, and blood vessel damage. Diabetes disease diagnosis via proper interpretation of the diabetes data is an important classification problem. In this study, a comparative pima-diabetes disease diagnosis was realized. For this purpose, a multilayer neural network structure which was trained by Levenberg-Marquardt (LM) algorithm and a probabilistic neural network structure were used. The results of the study were compared with the results of the pervious studies reported focusing on diabetes disease diagnosis and using the same UCI machine learning database.

A comparative study on thyroid disease diagnosis using neural networks

Thyroid hormones produced by the thyroid gland help regulation of the bodys metabolism. Abnormalities of thyroid function are usually related to production of too little thyroid hormone (hypothyroidism) or production of too much thyroid hormone (hyperthyroidism). Thyroid disease diagnosis via proper interpretation of the thyroid data is an important classification problem. In this study, a comparative thyroid disease diagnosis were realized by using multilayer, probabilistic, and learning vector quantization neural networks. For this purpose, thyroid disease dataset which is taken from UCI machine learning database was used.

Chest diseases diagnosis using artificial neural networks

Chronic obstructive pulmonary, pneumonia, asthma, tuberculosis, lung cancer diseases are the most important chest diseases. These chest diseases are important health problems in the world. In this study, a comparative chest diseases diagnosis was realized by using multilayer, probabilistic, learning vector quantization, and generalized regression neural networks. The chest diseases dataset were prepared by using patients epicrisis reports from a chest diseases hospitals database.

Tuberculosis Disease Diagnosis Using Artificial Neural Networks

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.

A Study on Hepatitis Disease Diagnosis Using Multilayer Neural Network with Levenberg Marquardt Training Algorithm

In this study, a hepatitis disease diagnosis study was realized using neural network structure. For this purpose, a multilayer neural network structure was used. Levenberg–Marquardt algorithm was used as training algorithm for the weights update of the neural network. The results of the study were compared with the results of the previous studies reported focusing on hepatitis disease diagnosis and using same UCI machine learning database. We obtained a classification accuracy of 91.87% via tenfold cross validation.

A Study on Chronic Obstructive Pulmonary Disease Diagnosis Using Multilayer Neural Networks

Chronic Obstructive Pulmonary Disease (COPD) is a disease state characterized by airflow limitation that is not fully reversible. The airflow limitation is usually both progressive and associated with an abnormal inflammatory response of the lungs to noxious particles or gases. COPD is important health problem and one of the most common illnesses in Turkey. It is generally accepted that cigarette smoking is the most important risk factor and genetic factors are believed to play a role in the individual susceptibility. In this study, a study on COPD 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. Back propagation with momentum and Levenberg–Marquardt algorithms were used for the training of the neural networks. The COPD dataset were prepared from a chest diseases hospital’s database using patient’s epicrisis reports.

A Comparative Study on Chronic Obstructive Pulmonary and Pneumonia Diseases Diagnosis using Neural Networks and Artificial Immune System

Millions of people are diagnosed every year with a chest disease in the world. Chronic obstructive pulmonary and pneumonia diseases are two of the most important chest diseases. And these are very common illnesses in Turkey. In this paper, a comparative study on chronic obstructive pulmonary and pneumonia diseases diagnosis was realized by using neural networks and artificial immune systems. For this purpose, three different neural networks structures and one artificial immune system were used. Used neural network structures in this study were multilayer, probabilistic, and learning vector quantization neural networks. The results of the study were compared with the results of the pervious similar studies reported focusing on chronic obstructive pulmonary and pneumonia diseases diagnosis. The chronic obstructive pulmonary and pneumonia diseases dataset were prepared from a chest diseases hospital’s database using patient’s epicrisis reports.

An approach based on probabilistic neural network for diagnosis of Mesothelioma's disease

Malignant mesothelioma (MM) is an aggressive progress tumor that results from mesotel cells and pleura usually incurs. The two important causes, in MM etiologies are known as asbestos and erionite, both mineral fibers. Environmental asbestos exposure and MM are one of the major public health problems of Turkey. In this study, two different probabilistic neural network (PNN) structures were used for MMs disease diagnosis. The PNN results were compared with the results of the multilayer and learning vector quantization neural networks focusing on MMs disease diagnosis and using same database. It was observed the PNN is the best classification with 96.30% accuracy obtained via 3-fold cross-validation. The MM disease dataset were prepared from a faculty of medicines database using new patients hospital reports from south east region of Turkey.

Diagnosis of chest diseases using artificial immune system

Chest diseases are one of the greatest health problems for people living in the developing world. Millions of people are diagnosed every year with a chest disease in the world. Chronic obstructive pulmonary, pneumonia, asthma, tuberculosis, lung cancer diseases are most important chest diseases and these are very common illnesses in Turkey. In this paper, a study on chest diseases diagnosis was realized by using artificial immune system. We obtained the classification accuracy with artificial immune system 93.84%. The result of the study was compared with the results of the previous similar studies reported focusing on chest diseases diagnosis. The chest diseases dataset were prepared from a chest diseases hospitals database using patients epicrisis reports.

An application of neural networks for harmonic coefficients and relative phase shifts detection

The varying the phase shifts will completely change the shape of the distorted wave, and may thus greatly affect the ability of the neural network to recognize harmonics. In this study, feed forward neural networks were used for the detection of the harmonic coefficients and relative phase shifts. The distorted wave including uniform distributed 5th, 7th, 11th, 13th, 17th, 19th, 23rd, 25th harmonics with up to 20^o relative phase shifts were simulated and used. Two neural networks were used for this purpose. One of the neural networks was used for the detection of the 5th, 7th, 11th, 13th harmonic coefficients and the other was used for the detection of the relative phase shifts of these harmonics. Scaled conjugate gradient algorithm was used as training algorithm for the weights update of the neural networks. The results show that these neural networks are applicable to detect each harmonic coefficient and relative phase shift effectively.