Trial of lung cancer determination by applying Deep Learning to human urine data and search of the target substance

Abstract

We tried to apply a Deep Learning to diagnose the lung cancer from a gas chromatography mass spectrometry data of human urine. The mother data consists of 28 healthy people and 39 lung cancer patient urine data sets. Each data set has 394 pieces of peak value as a feature. We applied unsupervised and supervised learning to four-layer neural network (NN). We got 97.0% accuracy of the diagnosis. We also used the trained NN for search the target substance.