Feature selection for the SVM: An application to hypertension diagnosis

Broiler chickens can benefit from machine learning: support vector machine analysis of observational epidemiological data

“…The risk factors identified compared favourably with those found using logistic regression (LR). This concordance between LR and SVM has been confirmed in subsequent studies [41][42][43]. Here, we provide a more detailed description of the use of SVM using a large set of routinely collected data to identify the risk factors for hock burn in broiler chickens.…”

Feature Selection Using Support Vector Machines and Independent Component Analysis for Wound Infection Detection by Electronic Nose

“…(30) This method ranks the features according to their influence on the decision hyperplane. The influence of the features is evaluated using the angle between the gradient of decision function of SVM and unit vectors that represent the indices of the individual features.…”

“…SVM is not only good at classification (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23) but also widely used in feature selection. (9,(24)(25)(26)(27)(28)(29)(30)(31)(32)(33) In this paper, a new method such as the embedded method for feature selection, which is specifically designed to work with SVM and ICA, is introduced. This method selects features that are least affected by background smell or least noisy for constructing the classification model.…”

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

“…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 …”