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

Authors:
Orhan Er;Abdullah Cetin Tanrikulu;Abdurrahman Abakay;Feyzullah Temurtas
Affiliations:
Bozok University, Department of Electrical and Electronics Engineering, 66200 Yozgat, Turkey;Dicle University, Faculty of Medicine, Department of Chest Diseases, 21100 Diyarbakir, Turkey;Dicle University, Faculty of Medicine, Department of Chest Diseases, 21100 Diyarbakir, Turkey;Bozok University, Department of Electrical and Electronics Engineering, 66200 Yozgat, Turkey
Venue:
Computers and Electrical Engineering
Year:
2012

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Abstract

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 MM's disease diagnosis. The PNN results were compared with the results of the multilayer and learning vector quantization neural networks focusing on MM's 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 medicine's database using new patient's hospital reports from south east region of Turkey.