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FRESA.CAD

Feature Selection Algorithms for Computer Aided Diagnosis.

Set of functions for: Conditioning, Feature Selection, Machine Learning, Cross-Validation, and Visual Evaluation

Table of Contents

Overview

The design of diagnostic or prognostic multivariate models via the selection of significantly discriminant features is complex.

FRESA.CAD provides a series of functions for: Data conditioning, Feature Selection, Machine Learning, Benchmarking, Visualization and Reporting.

Category Function(s) Purpose
Conditioning/Preprocessing nearestNeighborImpute() Impute missing values
Conditioning/Preprocessing FRESA.Scale() Data Scale/Normalization
Conditioning/Preprocessing featureAdjustment() Adjust variables removing collinearity
Conditioning/Preprocessing IDeA()/ILAA() Multicollinearity Mitigation
Feature Selection uniRankVar() Univariate Analysis
Feature Selection BSWiMS.model() Linear Model Subset Selection
Feature Selection univariate_BinEnsemble() Ensemble Select Top Features
Feature Selection univariate… Filter Select Top Features …
Machine Learning BSWiMS.model() Bootstrap Modeling
Machine Learning filteredFit() Pipeline ML: Scale/Filter/Transform/Learn
Machine Learning HLCM()/HLCM_EM() Latent-Class Based Modeling
Machine Learning GMVECluster() Unsupervised Clustering via GMVE
Benchmarking / Evaluation RandomCV() Random Holdout Validation
Benchmarking / Evaluation BinaryBenchmark() Binary Model Evaluation
Benchmarking / Evaluation OrdinalBenchmark() Ordinal Model Evaluation
Benchmarking / Evaluation CoxBenchmark() Cox-based Model Evaluation
Visualization / Reporting RRPlot() Survival Model Evaluation
Visualization / Reporting predictionStats_binary() Report Cross Validation Results Binary
Visualization / Reporting predictionStats_Ordinal() Report Cross Validation Results Ordinal
Visualization / Reporting predictionStats_survival() Report Cross Validation Results Survival

Besides the above listed functions the library provides predictors and wrappers of common machine learning methods, and many other auxiliary functions.

Installation

You can install the official release of the package from CRAN using:

install.packages("FRESA.CAD")

To install the development version from GitHub, use:

# Install 'devtools' package if you haven't already
install.packages("devtools")

# Install the package from GitHub
devtools::install_github("https://github.com/joseTamezPena/FRESA.CAD")

Usage

#Load the package
library(FRESA.CAD)

#For comprehensive evaluaiton of confusion tables
library("epiR")

# Example usage

data(stagec,package = "rpart")
options(na.action = 'na.pass')
dataCancer <- cbind(pgstat = stagec$pgstat,
                        pgtime = stagec$pgtime,
                        as.data.frame(
                          model.matrix(Surv(pgtime,pgstat) ~ .,stagec))[-1])

#Impute missing values
dataCancerImputed <- nearestNeighborImpute(dataCancer)
data(cancerVarNames)

UniRankFeaturesRaw <- univariateRankVariables(variableList = cancerVarNames,
                                                  formula = "pgstat ~ 1+pgtime",
                                                  Outcome = "pgstat",
                                                  data = dataCancer, 
                                                  categorizationType = "Raw", 
                                                  type = "LOGIT", 
                                                  rankingTest = "zIDI",
                                                  description = "Description",
                                                  uniType="Binary")
print(UniRankFeaturesRaw)

    # A simple BSIWMS Model

BSWiMSModel <- BSWiMS.model(formula = Surv(pgtime, pgstat) ~ 1, dataCancerImputed)
#The list of all models of the bootstrap forward selection 
print(BSWiMSModel$forward.selection.list)

#With FRESA.CAD we can do a leave-one-out using the list of models
pm <- ensemblePredict(BSWiMSModel$forward.selection.list,
                          dataCancer,predictType = "linear",type="LOGIT",Outcome="pgstat")

#Ploting the ROC with 95
pm <- plotModels.ROC(cbind(dataCancer$pgstat,
                               pm$ensemblePredict),
                     main=("LOO Forward Selection Median Predict"))

#The plotModels.ROC provides the diagnosis confusion matrix.
summary(epi.tests(pm$predictionTable))
    

More examples of FRESA.CAD usage can be found at: https://rpubs.com/J_Tamez

Contributing

Contributions are welcome! If you’d like to contribute to this project, please follow these guidelines:

- Fork the repository.

- Create a new branch: git checkout -b feature/new-feature.

- Make your changes and commit them: git commit -m 'Add new feature'.

- Push to the branch: git push origin feature/new-feature.

- Submit a pull request.

License

This project is licensed under the LGPL>=2.0.

Contact

For any questions or feedback, feel free to contact us at:

Email: jose.tamezpena@tec.mx

Twitter: @tamezpena

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.