Hybrid feature selection and classification model using high-dimensional data based on a metaheuristic algorithm for brain cancer diagnosis.

With highly significant results for all metrics (e.g., accuracy p = 0.002, AUC p = 0.001), SVM performs significantly better than Decision Tree, even when the strict Bonferroni-adjusted significance threshold (α = 0.003) is applied.

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On the other hand, considerable and practically significant performance advantages are indicated by the SVM–Decision Tree comparison, which shows huge effect sizes across all metrics (e.g., d = 1.187 for accuracy and d = 1.957 for AUC).

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