Prediction of The Gleason Group of Prostate Cancer from Clinical Biomarkers: Machine and Deep Learning from Tabular Data

Prostate Cancer (PC) has been shown to become an epidemic among men in the world. Early detection of PC is essential for treatment. Biopsies are often done to determine the Gleason score of PC which helps to predict the aggressiveness of PC. As biopsies may cause harm especially for old people, machine learning can be used to predict the Gleason grade of PC from clinical biomarkers that are typically structured in a table. In this paper, we present a comparative study of various machine learning methods to detect the Gleason grade of PC from tabular data. We also investigate the performance of advanced deep learning architectures specialized to deal with tabular data, such as TabNet, for this purpose. Moreover, we propose to build an ensemble of the best performing classifiers to grade PC with a promising performance.