Comparative Performance of Classical Statistical and Machine Learning Models for Melanoma Classification

Abstract

Early and accurate classification of melanoma is essential for improving patient outcomes and supporting clinical decision-making. Although numerous predictive models have been proposed, comparisons between classical statistical approaches and modern machine learning algorithms are often limited by heterogeneous analytical workflows and inconsistent validation strategies. This study aimed to compare the predictive performance of classical statistical and machine learning models for melanoma classification using a fully reproducible analytical framework. A retrospective observational study was conducted using the publicly available BCN20000 dermoscopic dataset from the ISIC Archive. After standardized data preprocessing, four routinely available clinical variables (age, sex, anatomical site and melanocytic status) were used to develop Logistic Regression, Generalized Additive Models, Random Forest and Extreme Gradient Boosting (XGBoost) classifiers. All models were trained and evaluated using the same stratified training/testing split, and their performance was assessed through discrimination, calibration and SHAP explainability analysis. Machine learning models, particularly XGBoost and Random Forest, achieved superior predictive performance compared with conventional statistical approaches, while patient age emerged as the most influential predictor of malignancy. The proposed framework provides a transparent and reproducible approach for objectively comparing predictive models and supports the development of accurate, interpretable, and reproducible clinical decision-support systems for melanoma classification.