Prediction of the early prognosis of the hepatectomized patient with hepatocellular carcinoma with a neural network

Prediction of posterior fossa tumor type in children by means of magnetic resonance image properties, spectroscopy, and neural networks

“…[12] They are especially well suited to nonlinear pattern recognition problems and have been applied over the past 5 years to a wide range of medical and scientific issues. [1,10,11,16,18,29,[30][31][32] The purpose of the present study was to compare the diagnostic accuracy of readings and predictions regarding pediatric posterior fossa tumors by a trained neuroradiologist (using preoperative MR images), with the predictions of various neural networks designed to train on MR image properties, age of the patient, and spectroscopic data.…”

Liver disorder diagnosis using linear, nonlinear and decision tree classification algorithms

“…The structure was designed with ten inputs, one hidden and one output layer. Initially multiple number of nodes (4,6,8,16,17,21) were tested in the hidden layer. The range of epochs was set from 10 to 1000.…”

“…These algorithms include artificial neural network (ANN), artificial immune system (AIS), case-based reasoning (CBR), classification and regression tree (CART), C4.5 and C5.0 decision trees, fuzzy logic (FL), rule-based reasoning (RBR) and support vector machines (SVMs) [1]. ANNs have been used by Hamamoto et al (1995) to predict early prognosis of hepatectomised patient with hepatocellular carcinoma [6], by Hayashi et al (2000) to diagnose hepatobiliary disorders [7], by Ozyilmaz and Yildirim (2003) to diagnose hepatitis disease [8], by Lee et al (2005) to classify liver cyst, hepatoma and cavernous haemangioma [9], by Yahagi (2005) to diagnose types of cirrhosis [10], by Azaid et al (2006) to classify fatty liver, liver cirrhosis and liver cancer [11], by Revett et al (2006) to perform mining of primary biliary cirrhosis [12] Babu and Suresh (2013) to classify liver disorder as sick and healthy [14][15][16][17], by Dong et al (2008) to calculate optimal value of cost parameter in order to minimize classification error [18], by Rouhani and Haghighi (2009), Ansari et al (2011) and Sartakhti et al (2015) to diagnose hepatitis disease [19][20][21], by Uttreshwar and Ghatol (2009) to specifically diagnose hepatitis B [22], by Bucak and Baki (2010) to classify liver disorders as hepatitis B, hepatitis C and cirrhosis [2], by Hashem et al (2010) to predict hepatic fibrosis extent in patients with HCV [23], by Revesz and Triplet (2010) to diagnosis primary biliary cirrhosis …”

An Introduction to Computational Intelligence in Healthcare: New Directions

“…Burke et al compared the prediction accuracy of a multilayer perceptron trained with the backpropagation learning algorithm and other statistical models for breast cancer survival [34]. Similarly, neural networks have been used for prognosis and assessment of the extent of hepatectomy of patients with hepatocellular carcinoma [35] and prognosis of coronary artery disease [36].…”