Computational prediction of metastasis of lung cancer patients from clinical features

Machine learning prediction of lung cancer metastasis from clinical data patients

First step: mapping of the feature values and normalization into the [0; 1] interval for each feature

Rscript normalization.r

Machine learning methods, instructions on Linux Ubuntu:

linear regression

/usr/bin/Rscript lin_reg.r

k-nearest neighbors

/usr/bin/Rscript knn.r

support vector machines

/usr/bin/Rscript svm.r

decision tree

/usr/bin/Rscript cart.r

one rule

/usr/bin/Rscript oner_class.r

naive bayes

/usr/bin/Rscript naive_bayes.r

random forest classification (top method)

/usr/bin/Rscript random_forest_class.r

deep neural network

th ann_script_val.lua

Paper

Chip M. Lynch, Victor H. van Berkel, Hermann B. Frieboes. "Application of unsupervised analysis techniques to lung cancer patient data". PLoS ONE 12(9): e0184370, 2017.