An Explainable Machine Learning Framework for Early Detection of Brain Tumours with Statistical Analysis of Clinical and Imaging Data

Abstract

Early and accurate detection of brain tumors is critical for improving patient outcomes, yet diagnostic delays and interpretability gaps limit the clinical adoption of artificial intelligence (AI) systems. This study proposes an explainable machine learning framework that integrates clinical and imaging data for early detection of brain tumors. Using a retrospective cohort of 1,248 patients, we developed and evaluated baseline statistical models, ensemble methods, and deep learning architectures, with systematic incorporation of SHAP values, Grad-CAM heatmaps, and patient-specific explanations. The hybrid model achieved an AUC-ROC of 0.97, accuracy of 93.2%, and well-calibrated predictions, outperforming imaging-only and tabular baselines. Explainability outputs demonstrated high concordance with radiologist annotations and were rated as clinically plausible. The framework maintained robust performance across tumor types, age groups, and imaging modalities, including CT-only cases. 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