Meta-Learning Techniques to Analyze the Raman Data for Optical Diagnosis of Oral Cancer Detection

Abstract

Recently, Machine Learning methods have shown great improvement while analyzing the biomedical data. Raman Spectroscopy (RS), a non-invasive technique, and widely used in screening to diagnose the oral cancer. In order to spot cancer in a smarter and faster way, we have employed Meta-Learning (ML) techniques to learn such as Bagging and Boosting on RS data. Further, we employed normal and tumor tissue class classification by Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) and Adaptive Boosting (AdaBoost) classifiers. The present study aims at examining the RS data with total 110 samples, including 57 tumor and 53 normal ones. To evaluate the performance, we have used the training samples to optimize, and testing samples to generalize the model parameters. The results show that the AdaBoost classifier with Bagging techniques showed the significant changes in accuracy.