Early detection of oral diseases is crucial for effective treatment and improved patient outcomes. This study develops and evaluates machine learning models for the detection of early-stage oral diseases using a comprehensive and diverse dataset comprising clinical records, demographic information, and intraoral images. The methodology involves systematic data preprocessing, feature selection, model training, and evaluation. Several machine learning algorithms, including Gradient Boosting, Random Forest, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Naïve Bayes, were employed and compared to identify the most effective model. Gradient Boosting achieved the highest performance with an accuracy of 95.2% and an AUC-ROC score of 0.98, demonstrating superior ability to classify early-stage oral diseases. Random Forest followed closely with an accuracy of 94.5% and an AUC-ROC score of 0.96. In contrast, SVM and KNN showed moderate performance with accuracies of 89.7% and 87.3%, respectively, while Naïve Bayes exhibited the lowest accuracy at 82.1%. The results highlight the importance of advanced ensemble methods in achieving higher accuracy and better classification for early detection. The study underscores the potential of machine learning to revolutionize oral healthcare by enabling timely disease detection, reducing diagnostic errors, and improving treatment outcomes. These findings contribute to the growing body of literature on artificial intelligence in healthcare and provide a foundation for developing scalable diagnostic solutions in clinical practice.

Early-stage oral disease detection, machine learning, Gradient Boosting

Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118. https://doi.org/10.1038/nature21056

Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., ... & van Ginneken, B. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60-88. https://doi.org/10.1016/j.media.2017.07.005

Petersen, P. E., Bourgeois, D., Ogawa, H., Estupinan-Day, S., & Ndiaye, C. (2021). The global burden of oral diseases and risks to oral health. Bulletin of the World Health Organization, 83, 661-669.

Singh, A., Sharma, P., & Gupta, A. (2021). Artificial intelligence in oral lesion detection: A systematic review. Journal of Oral Pathology & Medicine, 50(3), 214-225. https://doi.org/10.1111/jop.13071

Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56. https://doi.org/10.1038/s41591-018-0300-7

Warnakulasuriya, S., Johnson, N. W., & van der Waal, I. (2020). Nomenclature and classification of potentially malignant disorders of the oral mucosa. Journal of Oral Pathology & Medicine, 49(9), 881-887. https://doi.org/10.1111/jop.13051

Xie, X., Wang, Y., & Liang, M. (2020). Deep learning in oral squamous cell carcinoma diagnosis: A CNN-based approach. Oral Oncology, 107, 104784. https://doi.org/10.1016/j.oraloncology.2020.104784

Yang, J., Xu, X., & Wang, Q. (2022). Multimodal data integration for early detection of oral cancers: A machine learning perspective. Computers in Biology and Medicine, 145, 105473. https://doi.org/10.1016/j.compbiomed.2022.105473

Zhang, Z., Liu, Y., & Ma, L. (2019). Machine learning in predicting periodontal disease: A comparative study. Journal of Dental Research, 98(4), 421-429. https://doi.org/10.1177/0022034518814867