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Improving Oral Cancer Outcomes Through Machine Learning and Dimensionality Reduction
- Oral cancer poses a significant challenge in oncology, necessitating early detection and accurate prognosis for improved patient survival rates.
- Recent advancements in machine learning and data mining have transformed conventional diagnostic approaches, offering advanced tools for distinguishing between benign and malignant oral lesions.
- This study reviews state-of-the-art data mining methodologies like Neural Networks, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and ensemble learning techniques, specifically applied to oral cancer diagnosis and prognosis.
- A comprehensive analysis indicates that Neural Networks outperform other models, achieving an impressive 93.6% accuracy in predicting oral cancer.
- The study emphasizes the advantages of incorporating feature selection and dimensionality reduction methods to enhance model performance in diagnosing and prognosing oral cancer.
- These findings highlight the potential of advanced data mining techniques in facilitating early detection, optimizing treatment approaches, and ultimately enhancing patient outcomes in oral oncology.
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