A Comprehensive Review on Machine Learning Techniques for Oral Cancer Diagnosis and Prognosis

Abstract

Oral cancer persists as an unsolved global health dilemma with a very low survival rate and is begging for early diagnosis and prognosis. Machine learning systems and data mining techniques have revolutionised conventional techniques for diagnosing these cancers by facilitating automated, accurate, and fast detection of oral lesion differentiation. This paper asserts the application of a broad evaluation of existing techniques, which include machine learning algorithms in predicting oral cancer, for example, Support Vector Machines (SVM), K-Nearest Neighbours (KNN), Logistic Regression, Random Forest, Convolutional Neural Networks (CNN), Neural Networks (NN), and deep learning approaches. The frameworks have demonstrated much potential in accurately creating contrasts from histopathological images and clinical data and categorising benign against malignant forms. The models that are compared showed that in cases, especially where feature selection and dimensionality reduction techniques were enforced, neural networks and convolutional neural networks (CNNs) performed significantly better than linear and statistical machine learning methods. The addition of data preprocessing was found to increasingly improve the model performance. Methods like normalisation, augmentation, and feature extraction are crucial for model robustness and generalisation. The review paper also brings forth emphasis on achieving better predictive performance with combined learning and ensemble approaches. Major challenges still persist because of skewed data distributions, non-interpretability, and the combinatorial limitations of multimodal integration. Inclusion of clinical, genetic, and radiographic data apparently promises an obvious solution with explainable AI advancements supporting clinical decision-making. The article finally points at an untapped potential in the field, showing infinite promise for innovative technology for early detection, diagnosis, and efficient treatment – leading to increased survival.

How to cite this article:
Suraiya S, Rai A K. A Comprehensive Review on Machine Learning Techniques for Oral Cancer Diagnosis and Prognosis. J Adv Res Comp Graph Multim Tech. 2026; 8(2): 13-22.