A comparative analysis of machine learning and deep learning models for early oral cancer detection using multi-risk epidemiological data from multiple countries

Abstract

Purpose: This study aims to evaluate the effectiveness of machine learning, deep learning, and ensemble learning methods for the early diagnosis of oral cancer using multi-risk epidemiological and behavioural data collected from several countries. The main goal is to examine whether non-diagnostic factors can support early prediction before clear clinical signs appear. Design/Methodology/Approach: The study used a dataset containing 84,922 records, including pre-diagnosis and post-diagnosis variables. Post-diagnosis variables were used only for interpretation and analysis to avoid data leakage. Several models were tested, including Random Forest, Logistic Regression, Support Vector Machine, XGBoost, LightGBM, TabNet, MLP classifier, and a voting-based ensemble model. The models were evaluated using accuracy, recall, precision, F1-score, and ROC-AUC. Research Limitation: The main limitation is that the study did not use clinical diagnostic features, medical imaging, genetic markers, or laboratory biomarkers, which may improve prediction performance. Findings: The results showed that when only non-diagnostic epidemiological and behavioural features were used, all models achieved performance close to random classification. This means that early prediction of oral cancer using these features alone is difficult. The study also showed clear regional and economic differences among countries in oral cancer prevalence, feature importance, treatment costs, and productivity losses. Practical Implication: The findings suggest that epidemiological data should be combined with clinical and biomarker-based data to develop more accurate early diagnosis systems. Social Implication: The study highlights the need for better awareness, early screening programs, and improved access to diagnostic services, especially in developing countries. Originality/Value: This study provides a realistic evaluation of oral cancer early prediction using non-diagnostic data and emphasises the importance of avoiding data leakage in medical AI studies.