Feature Selection Algorithms for Computer Aided Diagnosis.
Set of functions for: Conditioning, Feature Selection, Machine Learning, Cross-Validation, and Visual Evaluation
Table of Contents OverviewThe 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.
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 SurvivalBesides the above listed functions the library provides predictors and wrappers of common machine learning methods, and many other auxiliary functions.
InstallationYou 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")#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
ContributingContributions are welcome! If youâd 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.
LicenseThis project is licensed under the LGPL>=2.0.
For any questions or feedback, feel free to contact us at:
Email: jose.tamezpena@tec.mx
Twitter: @tamezpena
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