This study presents an integrated machine learning framework for the early detection of oral cancer, leveraging both clinical data and high-resolution imaging. The research compared several algorithms, including logistic regression, decision trees, random forests, support vector machines, and convolutional neural networks, culminating in an ensemble model that combined clinical indicators with imaging features. Results demonstrate that while traditional models provided moderate diagnostic accuracy, advanced techniques, particularly the ensemble model, achieved superior performance with an accuracy of 91%, sensitivity of 89%, specificity of 92%, and an AUC of 93%. These findings highlight that multimodal data integration significantly enhances early detection capabilities, offering a robust and practical solution for clinical implementation. The proposed framework not only improves diagnostic precision but also supports timely interventions that can potentially reduce the morbidity and mortality associated with late-stage oral cancer.

Oral Cancer, Early Detection, Machine Learning, Ensemble Model, Clinical Data

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