Oral Cancer Detection Using Apriori Algorithm

Abstract: Oral cancer is among the 10 most common cancers in worldwide, and is especially seen in disadvantaged elderly males. Early prevention and detection and of oral cancer is critical, as it can increase the existence chances significantly, allow for simpler action and result in a better quality of life for survivors. In this article, the association rule mining algorithm, apriori is used to find the spread of oral cancer with the help of various inquiries and then measure the chance of survival of the patient. This is attained by extracting a set of rules among various laboratory tests and inquiries like RFT, fine-needle aspiration cytology (FNAC), Biopsy, Ultrasound, Sonography Test (USG), Chest x- ray or computed tomographyand survivability of the oral cancer patients. The rules clearly show that if FNAC, USG and Chest x-ray or computed tomography, then chance of existence is reduced. However, if RFT is now normal, the probability of survival is positive. If diagnostic-biopsy results in squamous-cell-carcinoma, then it clearly designate oral cancer, which may lead to high humanity if appropriate treatment is not initiated. The new results demonstrate that all the generated rules hold the highest confidence level, thereby, creation investigations very essential to understand the spread of cancer after medical examination for early detection and prevention of oral cancer.

Key-Words: Data Mining, Association Rule Mining, Apriori, Oral Cancer