Multi-national analysis and machine learning-base prediction of oral cancer trend and incidence globally, in South Asia and Bangladesh and global

Abstract

This proposal includes a comprehensive examination of oral cancer frequency and patterns from a multi-national perspective, particularly regarding Bangladesh. This study will mainly involve the usage of secondary data review; comparative analysis; and predictive modeling to compare aspects such as incidence rates, screening practices, and healthcare systems across nations. Machine Learning algorithms can predict the future trends of oral cancer until 2030. A detailed analysis of 54 oral cancer patients in Bangladesh has revealed essential demographic, clinical, and treatment factors. The majority of differences were observed in the aged of diagnosis, sex distribution, duration of treatment, frequency of screenings, incidence and survival rates. It was revealed that Bangladesh and Afghanistan recorded early diagnoses of cancer due the higher tobacco use while developed countries showed it is late due to the early detection techniques. In developed countries, early detection screening is common, unlike South Asian nations which practice symptomatic screening only. The rising death rates indicate inadequate medical facilities in South Asian countries like Afghanistan and Bangladesh. Predictive modeling indicates that by 2030 there will be a global rise in the incidence of oral cancer due to some risk factors like tobacco use, alcohol intake as well as human papillomavirus (HPV) exposure. Between the years 2023 and 2030, it is expected that the incidence in Bangladesh will increase by 4 percent. The leading public health programs should aim to increase the uptake of HPV vaccination, lowering betel nut consumption, and encourage exercise. These results call for an all-inclusive public health policy toward the prevention, early detection, and efficient management of oral carcinoma.