DESIGNING A NEURAL NETWORK MODEL USING K-MEANS CLUSTERING FOR RISK ANALYSIS OF LUNG CANCER DISEASE
Küçük Resim Yok
Tarih
2018
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Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
According to the World Health Organization report in 2004, lung cancer belongs to highest mortality rate cancertype compared to others. Genetics and early starting smoke etc. become the basis for lung cancer risk. In recentyears, lung cancer cases are increasing with the use of cigarettes at younger ages. One of the most important factorin the treatment of the disease is early diagnosis. Artificial intelligence methods, which have been used in manyareas in recent years, are also used for early diagnosis and imaging of diseases. In this study, a hybrid artificialneural network (ANN) model was designed to bring a different perspective to the use of multilayer ANN in theliterature for lung cancer risk prediction. Lung cancer risk factors were used as input data in predicting the disease.We tried to estimate the results using clustered data by K-means clustering algorithm and multi-layered ANNmethod. When the results obtained from the normalized and clustered data set are compared with the results in theliterature, the proposed model has a higher accuracy value than the other methods.
Açıklama
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Kaynak
Havacılık ve Uzay Teknolojileri Dergisi
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Scopus Q Değeri
Cilt
11
Sayı
2
