Orhan ER, A. Çetin TANRİKULU, Abdurrahman ABAKAY

Use of artificial intelligence techniques for diagnosis of malignant pleural mesothelioma Malign plevral mezotelyoma tanısı için yapay zeka teknikleri kullanımı

Amaç: İnsanların beyin zarında bulunan, asbestos ve asbestiform liflerine maruz kalmakla oluşan kötü huylu plevral Mezotelyoma, oldukça saldırgan bir tümördür. Düşük seviyeli çevresel erionite fibrous zeolite’e maruz bırakılmış Türkiye’deki bazı kasabalarda Mezotelyoma görülme oranı oldukça yüksektir.Yöntemler: Bu çalışmada Mezotelyoma hastalığı teşhisi yapay bağışıklık sistemi kullanımı ile gerçekleştirilmiştir. Bununla beraber yapay bağışıklık sistemi sonuçları, aynı veri tabanını kullanan, Mezotelyoma hastalığının teşhisine odaklanmış çok katmanlı yapay sinir ağı sonuçları ile karşılaştırılmıştır. Mezotelyoma hastalığı veri seti, hastaların hastane raporlarını kullanan tıp fakültesi veri tabanından alınmıştır.Bulgular: Yapay bağışıklık sistemi tarafından hastalık teşhisi için %97,74 doğruluk oranında bir performans elde edilmiştir. Yapay bağışıklık sistemi algoritmasının doğruluk sonuçları çok katmanlı yapay sinir ağı algoritmasından çok daha iyi olduğu görülmüştür.Sonuç: Bu sistem uzmana, sağlıklı ve hasta kişiyi sınıflandırma sürecinde doğru teşhisi bulma yönünde iyi bir performans sağlar. Böylece bu yapı ile doğru teşhis sonucuna ulaşmada doktorlara bir karar destek sistemi olarak yardımcı olur

Anahtar Kelimeler:

Kötü huylu plevral mezotelyoma hastalığının teşhisi, yapay bağışıklık sistemi, makine öğrenme tabanlı karar destek sistemi

Use of artificial intelligence techniques for diagnosis of malignant pleural mesothelioma

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. Key words: malignant pleural mesothelioma disease diagnosis, artificial immune system, machine learning based decision support system.

Keywords:

malignant pleural mesothelioma disease diagnosis, artificial immune system, machine learning based decision support system,

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