Brain Cancer Diagnosis-Association Rule Based Computational Intelligence Approach

This research employs a computational intelligence based approach to identify the risk factors of brain cancer. More specifically this research utilizes association rule mining techniques to determine the risk factors derived from the brain cancer literature. The research also develops a novel database extracting data from existing literature. Arguably, the outcomes may aid designing brain cancer diagnostic systems and contribute to early diagnosis of the mortal disease. Additionally, it demonstrates the effectiveness of computational intelligence techniques in cancer disease diagnosis. The benefits of this research could apply to different stakeholders such as the CAD (Computer aided diagnosis) designers and the data mining community, the medical field and in particular general practitioners as well as the general public.