Prediction methods for oral diseases

Abstract

With the increasing advancement of science and technology, especially the rapid development of electronic computer technology, machine learning methods have obvious advantages in predicting oral diseases. Especially in the research on oral cancer, this article uses a variety of machine learning methods for prediction and has achieved some remarkable results to provide a basis for subsequent disease treatment. In the prediction analysis of oral cancer, logistic regression, Cox regression, and machine learning algorithms were used to make a preliminary judgment on the triggering factors of oral cancer. The main triggers were dietary habits, betel nut chewing, smoking, and excessive drinking. Secondly, this article explores solutions to oral cancer prediction problems. By comparing various methods, it is concluded that Cox regression is generally used to predict patient survival or recurrence rate, while logistic regression and machine learning algorithms are used to induce Judgment and evaluation of factors, among which machine learning algorithms mainly identify factors with a wide range and high complexity, while logistic regression is used to identify some specific factors. Oral cancer is a malignant tumor. Therefore, early prediction and treatment of oral cancer are not only crucial to the patient's survival rate and oral quality but also play a major role in the progress of medicine and the development of human society.