An Intelligent System for the Early Detection of Prostate Cancer

Prostate cancer ranks as the fourth most prevalent cancer in Canada, with mortality rates exhibiting exponential growth among men aged 50 and older and, thus, poses a substantial public health challenge. This paper aims to demonstrate the potential of machine learning in predicting prostate cancer based on many of the risk factors as well as attempting to study some of the commonly used machine learning models as a predictor for prostate cancer. In this study, we assess the performance of diverse models, including Random Forest (RF), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), General Linear Model (GLM), and a deep learning approach known as Feed Forward Network (FFN). Notably, our results reveal that the FFN model surpasses the other models in its predictive accuracy for prostate cancer.