Improving Oral Cancer Outcomes Through Machine Learning and Dimensionality Reduction

TL;DR

This paper reviews machine learning methods for oral cancer diagnosis, highlighting neural networks' superior accuracy and the benefits of feature selection and dimensionality reduction in improving predictive performance.

Contribution

It provides a comprehensive comparison of data mining techniques for oral cancer diagnosis, emphasizing neural networks and the role of dimensionality reduction.

Findings

Neural Networks achieved 93.6% accuracy in classification.

Feature selection and dimensionality reduction improve model performance.

Machine learning techniques can enhance early detection and treatment strategies.

Abstract

Oral cancer presents a formidable challenge in oncology, necessitating early diagnosis and accurate prognosis to enhance patient survival rates. Recent advancements in machine learning and data mining have revolutionized traditional diagnostic methodologies, providing sophisticated and automated tools for differentiating between benign and malignant oral lesions. This study presents a comprehensive review of cutting-edge data mining methodologies, including Neural Networks, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and ensemble learning techniques, specifically applied to the diagnosis and prognosis of oral cancer. Through a rigorous comparative analysis, our findings reveal that Neural Networks surpass other models, achieving an impressive classification accuracy of 93,6 % in predicting oral cancer. Furthermore, we underscore the potential benefits of integrating feature selection and dimensionality reduction techniques to enhance model performance. These insights underscore the significant promise of advanced data mining techniques in bolstering early detection, optimizing treatment strategies, and ultimately improving patient outcomes in the realm of oral oncology.