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We used different machine learning approaches to build models for detecting and visualizing important prognostic indicators of breast cancer survival rate. This repository contains R source codes for 5 steps which are, model evaluation, Random Forest further modelling, variable importance, decision tree and survival analysis. These can be a pipe…

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MoganaD/Machine-Learning-on-Breast-Cancer-Survival-Prediction

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Machine-Learning-on-Breast-Cancer-Survival-Prediction

We used different machine learning approaches to build models for detecting and visualizing important prognostic indicators of breast cancer survival rate. This repository contains R source codes for 5 steps which are, model evaluation, Random Forest further modelling, variable importance, decision tree and survival analysis. These can be a pipeline for researcher who are interested to conduct studies on survival prediction of any type of cancers using multi model data.

The pipeline is as follows:

1. Model evaluation using 6 different algorithms in R

1.1)Random Forest

1.2)Decision Tree

1.3)Support Vector Machine

1.4)Logistic Regression

1.5)Neural Networks

1.6)Extreme Gradient Boost

2. Random Forest Further modelling in R

2.1)Selection of best ntree

2.2)Model evaluation for all the clusters

2.3)Calibration plot using Phyton 3

3. Variable Importance in R

3.1)Using VSURF

3.2)Using randomForestExplainer

4. Decision Tree in R

5. Survival analysis in R

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We used different machine learning approaches to build models for detecting and visualizing important prognostic indicators of breast cancer survival rate. This repository contains R source codes for 5 steps which are, model evaluation, Random Forest further modelling, variable importance, decision tree and survival analysis. These can be a pipe…

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