Prediction of Oral Cancer Treatment Plan using Machine Learning

Abstract

Machine learning (ML) is a sub-branch of artificial intelligence (AI) that employs statistical, optimization, and fuzzy techniques to learn from past data and detect patterns from large, noisy, and complex datasets. Oral cancer treatment for a patient with mouth or throat cancer is crucial. Oral cancer treatment analyses the development of cancer cells in the tissues of the mouth or throat post-operation. In post-operation care, the mouth opening of the patient depends on the surgery performed. Further, how often should the patient perform the exercise with a jaw stretcher machine and consult the doctor? In this paper, we employed different machine learning algorithms to predict the treatment plan for the patient. We introduced the one-way ANOVA test to identify the optimal feature set. Our proposed approach applies different ML algorithms with ten-fold cross-validations to suggest a post-operation treatment plan for a patient. The experimental results show the highest accuracy with the polynomial regression and the hybrid approach.