Using Machine Learning for Classification of Cancer Cells from Raman Spectroscopy

Since cancer represents one of the leading causes of death worldwide, the development of approaches capable of discerning healthy from diseased cells would be of fundamental importance to support diagnostic and screening techniques. Raman spectroscopy is the most effective molecular analysis technique currently available and provides information on the molecular composition, bonds, chemical environment, phase, and crystalline structure of the samples under examination. This work exploits a combination of Raman spectroscopy and machine learning models to discriminate patients’ liver cells between tumor and non-tumor. The research uses real patient data, provided by the Center for Nanophotonics and Optoelectronics for Human Health (CNOS), which analyzed the cells of a patient with liver cancer. Specifically, the dataset has been built through a long data collection process, which first involved the analysis of the cells with Raman spectroscopy and then the training of two classifiers, Decision Tree and Random Forest. The results show good performance for the trained classifiers, especially those relating to the Random Forest, which reaches an accuracy of 90%.