Prediction of Oral Cancer Incidence in Hunan Using a Time-Series Prediction Model Based on Logistic Regression Modeling

Abstract

Scientific and reliable prediction of the incidence of oral cancer is very meaningful, which can assist the medical industry to carry out effective prevention and treatment measures, and also serve as a reference and basis for the formulation of related health policies. Therefore, this paper mainly explores the prediction method based on the Logistic Regression model to predict and analyze the number of oral cancer incidences. The study in this paper found that age, gender, and betel nut consumption were associated with the incidence of oral cancer. Among them, males and middle-aged and older age groups were more susceptible. The time-series prediction indicates that the model accurately predicts oral cancer incidence in multifactorial samples, which provides effective preventive and curative measures for the healthcare industry and serves as a basis for health policy. Future studies can improve the accuracy through comprehensive data collection and advanced model optimization. The results of the study provide a reference for early prevention and intervention of oral cancer, which can help formulate health policies to serve public health and individual health.