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+# 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"](https://doi.org/10.1371/journal.pone.0184370). PLoS ONE 12(9): e0184370, 2017.
+
+## Question on Medical Science Stack Exchange
+[Can tumor size (T) and presence of cancer in the lymph nodes (N) in patients with lung cancer be identified on the first visit?](https://medicalsciences.stackexchange.com/questions/18040/can-tumor-size-t-and-presence-of-cancer-in-the-lymph-nodes-n-in-patients-wit)