Classification of the likelihood of colon cancer with machine learning techniques using FTIR signals obtained from plasma

Abstract

Colon cancer is one of the major causes of human mortality worldwide and the same can be said for Turkey. Various methods are used for the determination of cancer. One of these methods is Fourier transform infrared (FTIR) spectroscopy, which has the ability to reveal biochemical changes. The most common features used to distinguish patients with cancer and healthy subjects are peak densities, peak height ratios, and peak area ratios. The greatest challenge of studies conducted to distinguish cancer patients from healthy subjects using FTIR signals is that the signals of cancer patients and healthy subjects are similar. In the current study, a method in which the area and height ratios of the FTIR signal, as well as various statistical features, are proposed in order to overcome this difficulty. Blood samples (plasma) were collected from 30 colon cancer patients and 40 healthy subjects, and FTIR measurements were performed. A total of 16 features were obtained, including five height ratios, five area ratios, and six statistical features, from each FTIR signal. The 16 features were classified with a multilayer perceptron neural network and support vector machines using cross-validation and their performances were then compared. The current study demonstrated that different features obtained from plasma FTIR spectra can be used together in order to distinguish colon cancer patients from healthy individuals.