Diagnosis of Pancreatic Cancer by Pattern Recognition Methods Using Gene Expression Profiles
[ N/A ]
Tarih
2017
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ieee
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Pancreatic cancer is the fourth most common cause of cancer-related deaths across the globe and it is one of the most difficult cancer types to recognize early. Early diagnosis of pancreatic cancer is crucial to increase survival for patients. In this study, it was tried to be estimated that persons were pancreatic cancer or healthy using microarray gene expression profile. In accordance with this purpose, Anova method was used to reduce the size of high-dimensional pancreatic cancer gene expression profile and eliminate redundant features. Reducedsize pancreas cancer gene expression profiles were classified by k-nearest neighbor (k-NN) and artificial neural network (ANN) algorithms. The classification accuracy is %82.7 and 84.6% with k-NN, ANN respectively. The promising results indicate that pancreatic cancer can be diagnosed with high accuracy.
Açıklama
2017 International Artificial Intelligence and Data Processing Symposium (IDAP) -- SEP 16-17, 2017 -- Malatya, TURKEY
Anahtar Kelimeler
Gene expression profile, pancreatic cancer, artificial neural network, k-nearest neighbor
Kaynak
2017 International Artificial Intelligence and Data Processing Symposium (Idap)
WoS Q Değeri
N/A
Scopus Q Değeri
N/A