Analysis of energy and major components in chromatographic signals for the diagnosis of prostate cancer

The prostate exam is an early detection tool to prevent prostate cancer and the main diagnostic tools for obtaining signs are generally invasive. This article tries chromatographic signals from the urine of prostate cancer patients and control patients as a non-invasive examination proposal. For this purpose, methodologically, urine samples are taken, digitized in chromatograms, treated with mathematical techniques and classified. The mathematical techniques are time normalization, dead time elimination, baseline correction, noise elimination, and peak alignment. Classification techniques analyze energy, in the domain of time and frequency, and the main components in sedimentation graphs and scores. As a result, the chromatographic signal is characterized and identifies the characteristic curve that represents the signal of prostate cancer patients and control patients. The data structure shows a cluster distribution of 88.88% of the vectors for the control patients. In the case of prostate cancer patients, the distribution of data is in clusters around the area defined by control patients. This characterization demarcates signal classification regions to diagnose possible prostate cancer patients, validating the relationship between the chromatographic signal and cancer.