Mammographic Mass Lesion Classification Using Supervised Learning

App Description

This Shiny app allows users to explore mammographic mass data and fit models for predicting mass status through repeated cross-validated supervised learning models.

About the data

The data used in this app is from a study of mammographic mass lesions performed at University Erlangen-Nuremberg between 2003 and 2006. The research was performed with the goal of reducing unnecessary biopsies by using computer-aided diagnosis systems to predict the Severity status, benign or malignant, of lesions.

Citation: M. Elter, R. Schulz-Wendtland and T. Wittenberg (2007) The prediction of breast cancer biopsy outcomes using two CAD approaches that both emphasize an intelligible decision process. Medical Physics 34(11), pp. 4164-4172 Source

List of Packages

caret, rpart.plot, shiny, shinythemes, shinyWidgets, tidyverse

Code for Installing All the Packages

install.packages(c("caret", "rpart.plot", "shiny", "shinythemes", "shinyWidgets", "tidyverse"))

Code to Launch App

shiny::runGitHub(repo = "MassClassification", username = "akoncsugit",
ref = "main", subdir = "/app/", launch.browser = TRUE)