Deep learning for liver disease prediction
Küçük Resim Yok
Tarih
2022
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer Science and Business Media Deutschland GmbH
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Mining meaningful information from huge medical datasets is a key aspect of automated disease diagnosis. In recent years, liver disease has emerged as one of the commonly occurring diseases across the world. In this paper, a Convolutional Neural Network (CNN) based model is proposed for the identification of liver disease. Furthermore, the performance of CNN was also compared with traditional machine learning approaches, which include Naive Bayes (NB), Support Vector Machine (SVM), K-nearest Neighbors (KNN), and Logistic Regression (LR). For evaluation, two datasets were used: BUPA and ILPD. The experimental results showed that CNN was effective for the classification of liver disease, which produced an accuracy of 75.55%, and 72.00% on the BUPA and ILPD datasets, respectively. © 2022, Springer Nature Switzerland AG.
Açıklama
Anahtar Kelimeler
Convolutional Neural Networks, Disease Classification, Liver Diseases Classification, Machine Learning
Kaynak
Communications in Computer and Information Science
WoS Q Değeri
Scopus Q Değeri
Q4
Cilt
1543
Sayı
Künye
Mutlu, E. N., Devim, A., Hameed, A. A., & Jamil, A. (2022). Deep learning for liver disease prediction doi:10.1007/978-3-031-04112-9_7 Retrieved from www.scopus.com
