https://github.com/MoganaD/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.
https://github.com/MoganaD/Machine-Learning-on-Breast-Cancer-Survival-Prediction

breast-cancer-prediction cancer-data decision-trees machine-learning prediction-model survival-analysis variable-importance

Last synced: 2 months ago
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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 pipeline for researcher who are interested to conduct studies on survival prediction of any type of cancers using multi model data.

• Host: GitHub
• URL: https://github.com/MoganaD/Machine-Learning-on-Breast-Cancer-Survival-Prediction
• Owner: MoganaD
• Created: 2018-09-03T02:05:46.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2020-11-01T06:57:59.000Z (about 4 years ago)
• Last Synced: 2024-08-04T10:01:25.042Z (6 months ago)
• Topics: breast-cancer-prediction, cancer-data, decision-trees, machine-learning, prediction-model, survival-analysis, variable-importance
• Language: R
• Homepage:
• Size: 253 KB
• Stars: 11
• Watchers: 1
• Forks: 7
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-ai-cancer - MoganaD/Machine-Learning-on-Breast-Cancer-Survival-Prediction - Model evaluation, Random Forest further modelling, variable importance, decision tree, and survival analysis in R (Code / Repositories)

README

        
# 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 (_Model Evaluation.md_)

  ### 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 (_Random Forest.md_)

  ### 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 (_Variable importance.md_)

  ### 3.1 Using _VSURF_

  ### 3.2 Using _randomForestExplainer_

## 4. Decision Tree in R (_Decision tree.md_)

## 5. Survival analysis in R (_Survival analysis.md_)