Presently, no effective tool exists for early diagnosis and treatment of oral cancer. Here, we describe an approach for cancer detection and prevention based on analysis using association rule mining. The data analyzed are pertaining to clinical symptoms, history of addiction, co-morbid condition and survivability of the cancer patients. The extracted rules are useful in taking clinical judgments and making right decisions related to the disease. The results shown here are promising and show the potential use of this approach toward eventual development of diagnostic assay and treatment with sufficient support and confidence suitable for detection of early-stage oral cancer.