## Similar protocols

## Protocol publication

[…] To prevent overfitting and artificial accuracy improvement due to the use of the same data for training and testing of classifiers, a 5-fold cross-validation strategy was followed to train and evaluate the 10 classifiers. The total sample was divided into 5 proportional sub-samples. In each of the 5 steps, 4/5 of the sample was used for training and 1/5 for testing. Test results for the 5 runs, gathered from the 5 test samples, were then considered for further comparisons. The performances (total accuracy, sensitivity, specificity, AUC and Press' Q) of the different classifiers where compared with Friedman's test followed by Dunn's post-hoc multiple comparisons of mean ranks for paired samples. Statistical significance was assumed for p < 0.05. To avoid biases from the data sets, equal a priori classification probabilities were used for Linear Discriminant Analysis, Quadratic Discriminant Analysis and Logistic Regression. Neural Networks, Support Vector Machines, Classification trees and Random forests used settings that are most frequently employed in practical data mining applications as follows. The Multilayer Perceptron was trained with 11 inputs (one for each predictor) in the input layer, 1 hidden layer with 4-7 neurons and a hyperbolic tangent activation function. The number of neurons in the hidden layer was iteratively adjusted by the software to minimize classification errors in the train data set. The activation function for the output layer was the Softmax with a cross-entropy error function. Synaptic weights were obtained from a 80%:20% train: test setup. The Radial Basis Function Neural Network had 11 inputs, one hidden layer with 2-8 neurons and a Softmax activation function. The activation function for the output layer was the identity function with a sum of squares error function. The Gaussian function was the kernel used in the SVM. Cost (c) and γ parameters were optimized by a linear grid search in the intervals [2-3; 215] for c and [2-15; 23] for γ, followed by cross-validation of each of the SVM obtained in the 5 train sets. The classification function was the sign of the optimum margin of separation. CHAID, CART and QUEST classification trees used α to split and α to merge of 0.05, with 10 intervals. Tree growth and pruning of CART were set with a minimum parent size of 5 and minimum child size of 1. Classification priors for both trees were fixed at 0.5:0.5. Random Forests were composed of 500 CART trees with 2-9 predictors per tree cross-validation optimization. The Predictive Analytic Software (PASW) Statistics (v. 18, **SPSS** Inc., Chicago, Il) was used for Discriminant Analysis, Logistic Regression, Neural Networks and Classification Trees. Support Vector Machines and Random Forests were performed with R (v. 2.8, CRAN) with the e1071 [] and **randomForest** [] packages, respectively. […]

## Pipeline specifications

Software tools | SPSS, randomforest |
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Application | Miscellaneous |

Diseases | Dementia |