Risk factors for prostate cancer were identified through extensive research of literature and data was retrieved from both literatures and repositories. The research applies data mining techniques to the medical literatures and evidences on prostate cancer, with the aim to unravel the relationships between the presence of having multiple lifestyle factors and prostate cancer effective of occurrence of multiple factors. The research is to establish a possible predictive model based on theorized and proven risk factors and associations used in prostate cancer research. This paper describes the use of data mining algorithms on the risk factors to identify hidden knowledge. Firstly, an association rule mining algorithm is employed to identify the significant risk factors for the predictive modeling, based on the support level in terms of research materials used and confidence values. Secondly, the chosen factors were combined, modelled and visually represented to show their probability risks in relation to each other and the disease.