Use of artificial intelligence techniques for diagnosis of malignant pleural mesothelioma

Abstract

Objective: Malignant pleural mesothelioma is a highly aggressive tumor of the serous membranes, which in humans results from exposure to asbestos and asbestiform fibers. The incidence of malignant mesothelioma is extremely high in some Turkish villages where there is a low-level environmental exposure to erionite, a fibrous zeolite. Therefore epidemiological studies are difficult to perform in Turkey. Methods: In this paper, a study on malignant pleural mesothelioma disease diagnosis was realized by using artificial immune system. Also, the artificial immune system result was compared with the result of the multi-layer neural network focusing on malignant pleural mesothelioma disease diagnosis and using same database. The malignant pleural mesothelioma disease dataset were prepared from a faculty of medicine's database using patient's hospital reports. Results: 97.74% accuracy performance is obtained by artificial immune system. The accuracy results of artificial immune system algorithm are much better than the accuracy results of multi-layer neural network algorithm. Conclusion: This system is capable of conducting the classification process with a good performance to help the expert while deciding the healthy and patient subjects. So, this structure can be helpful as learning based decision support system for contributing to the doctors in their diagnosis decisions.

Relationship between Pretreatment Serum Albumin Levels with the Risk of Malignant Pleural Mesothelioma

“…The most of the work using this MPM dataset were diagnostic works which are based on various classifiers [23][24]55]. Object was to classify or diagnosis the disease with minimum misclassification rate.…”

“…Several studies were carried out about MPM epidemiology, clinics in south east of Turkey [20][21][22]. There are many studies on MPM disease diagnosis using artificial intelligence techniques also like, probability neural networks (PNNs), learning vector quantization (LVQ) [23], artificial immune system (AIS) and multi-layer neural network (MLNN) [24] with prognostic data. MPM is a very rare type of malignant and fatal disease with a poor prognosis.…”

“…The major objective of this study is to explore a relationship between SA and the other bio medical parameters of MPM patients. Many authors had used various classification techniques on this dataset for MPM disease diagnosis [23,24], but probably, advance regression or probabilistic modeling techniques are not been used under proper modeling scheme.…”

Osteoporoz Ön Tanısının Olasılıksal Sinir Ağları (OSA) Yardımıyla Gerçekleştirilmesi

“…Bu yetenekleri geleneksel programlama yöntemleri ile gerçekleştirmek oldukça zordur veya mümkün değildir. Bu nedenle, yapay sinir ağlarının, programlanması çok zor olan veya mümkün olmayan olaylar için geliştirilmiş adaptif bilgi işleme ile ilgilenen bir bilgisayar bilim dalı olduğu söylenebilir (Er, 2015).…”

An application of multilayer neural network on hepatitis disease diagnosis using approximations of sigmoid activation function

“…The hardware implementations of hybrid systems require large scale multipliers and chip resources. For the disease diagnosis systems, multilayer neural networks (MLNNs) have been the most commonly used tools [17]. Different types of learning algorithms can be used to train MLNN [18,22].…”