Although cancer research has generally been clinical and/or biological in nature, in recent years, data driven analytic research has become a common complement. In medical domains where data and analytics driven research is successfully applied, new and novel research directions are identified to further advance the clinical and biological studies. Specifically, it is the combination of the serious effects of cancer, the promising results of prior analytical research in related fields, the potential benefits of the expected research outcomes and the desire to further understand the nature of cancer that provided the motivation for this research effort. Therefore, the main objective of this research has been to take advantage of the available data mining tools and techniques to develop accurate prediction models for prostate cancer survivability, and to explain the prioritized importance of the prognostic factors.