Brainoread

This source code is a tool which is a standalone binary classifier using machine learning algorithms from R and Java. Currently used for diagnosis of patients suffering from brain disorders.

Visible methods

1) getBestModel(x, y, iter = 100, verboseIter = FALSE){…}

####file : getBestModel.r

This function takes features(x) and labes(y) of training dataset as input, performs data preprocessing (replace NA and NaN with 0). Evaluate train data for each model with stratified sampling (70% - 30%) “iter” times, which is 100 by default. Function returns a dataframe with model-name, environment, averaged AUC, specificity, sensitivity, PPV, NPV. This function calls hidden methods getModelList() and getAvgAUC() to get dataframe to be return with default 0 values and get averaged values for “iter” iteration for each model in dataframe.

2) getTrainModel(x, y, methodName, verboseIter = FALSE, tuneLength = 10){…}

file : trainPredict.r

This function takes same features(x), and labels(y) of training dataset as input with extra argument methodName – this is a row from dataframe returned by getBestModel(). This function train model if the environment is R then tuneLength parameter is used. For R environment trainControl() and train() from caret are used, and if environment is JAVA then train dataset (features and labels) is written in file and calls the hidden method javaCall(), this function calls java methods using rJava library. Final model is written in file (currentModel.rds for R and currentModel.obj for java). This function returns vector with model-name and environment.

3) getPredictProb(model, testData){…}

file : trainPredict.r

This function uses model (vector return from getTrainModel) and test dataset as input. If environment is R model written in currentModel.rds is read and predict() method from caret is used to get prediction probabilities, and for JAVA environment test dataset is written in file with extra result file and call is made to javaCall() method, to get prediction probabilities java class writes result in result_prob.csv file which is read by hidden method getPredFromFile(). Function returns dataframe with both class probabilities.

4) getLastModel(){…}

file : trainPredict.r

Method getTrainModel() creates one additional file info.csv which contains labels of class, model-name and environment information. This function reads that file and returns a vector same as getTrainModel() returns.

Hidden Methods

1) getModelList(){…}

file : getAUC.r

This function creates a dataframe of model-name, environment and accuracy value fields with default 0 and returns that dataframe.

2) getAvgAUC(x, y, methodName, iter, verboseIter){…}

file : getAUC.r

This method takes features(x), labels(y), method-name (row of model-list) and number of iterations to be performed. Function returns vector with averaged values of accuracy values. This function perform stratified sampling (70% - 30%) 70% is used for training and 30% is used for testing. If environment is R then train() and predict() from caret is used. If environment is JAVA then train and test dataset is written in files with addition result file, and a call is made to javaCall() method. Java writes result in file which is read by getPredFromFile() method. AUC is caluculated using caTools::colAUC() method and rest values are calculated using caret::confusionMatrix() method.

3) removeNaN(x){…}

file : getAUC.r

This file replaces NaN values with 0 from dataframe and returns that dataframe .

4) getPredFromFile(){…}

file : getAUC.r

Java writes predictions of positive class in result_prob.csv. This functions reads this file and place probabilities of negative class and returns dataframe with both class probabilities.

5) javaCall (modelName, train = FALSE, test = FALSE){…}

file :javaCall.R

This function uses rJava to create object of java class file and calls method of that class instance. This method uses model-name, with train and test arguments. If train and test both are false then java methods performs both training and testing (used for stratified sampling). If train is true then only train model and save in model in file, and if test is true then read model saved in file test the dataset and write prediction in result_prob.csv

Demo script is written in test.r file with dementia dataset (demen.csv)