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Table 4 Mean testing accuracy of individual classification models after 50 replicates with standard deviation in the brackets

From: Ensemble machine learning approach for screening of coronary heart disease based on echocardiography and risk factors

Model Accuracy
logistic regression \(67.7\% (0.034)\)
penalized logistic regression \(70.8\% (0.022)\)
cumulative probability model \(68.6\% (0.035)\)
random forest \(59.2\% (0.034)\)
weighted subspace random forest \(59.3\% (0.033)\)
SVM with class weight \(70.2\% (0.043)\)
SVM with polynomial kernel \(66.3\% (0.041)\)
SVM with radial kernel \(63.7\% (0.041)\)
K-nearest neighbor \(58.2\% (0.037)\)
LDA \(69.6\% (0.048)\)
sparsed LDA \(58.8\% (0.036)\)
naive Bayes \(64.4\% (0.024)\)
Bayes generalized linear model \(68.0\% (0.031)\)
Gaussian process with polynomial kernel \(70.1\% (0.035)\)
Gaussian process with radial kernel \(65.2\% (0.029)\)
Neural network \(62.8\% (0.043)\)
Monotone multi-layer perceptron neural network \(69.2\% (0.026)\)
model average neural network \(65.1\% (0.035)\)
stochastic gradient boosting \(57.8\% (0.027)\)