Pre-existing Statistical Predictive Models for Early Detection of Cancer: A Comprehension review

Early detection of cancer significantly enhances treatment success and survival rates. A wide range of statistical and machine learning models have been developed to identify high-risk individuals and detect malignancies at early stages. This review provides a comprehensive overview of established predictive models, including logistic regression, Cox proportional hazards models, Bayesian networks, and machine learning techniques such as random forests, support vector machines, and deep neural networks. We examine each model's mathematical foundation, clinical applications, and predictive performance, highlighting their respective advantages and limitations. Common challenges such as data heterogeneity, interpretability, and model generalizability are also discussed. The review concludes with future directions aimed at improving model integration, transparency, and real-world clinical impact in cancer early detection.