Supplementary Material for: Feature Importance Analysis and Machine Learning for Alzheimer's Disease Early Detection: Feature Fusion of Hippocampus, Entorhinal Cortex, and Standardized Uptake Value Ratio

Introduction: Alzheimer's disease (AD) is a progressive neurological disorder characterized by mild memory loss and ranks as a leading cause of mortality in the United States, accounting for approximately 120,000 deaths per year. It is also the primary form of dementia. Early detection is critical for timely intervention, as the neurodegenerative process often starts 15 to 20 years before cognitive symptoms manifest. This study focuses on determining feature importance in AD classification using fused texture features from 3D Magnetic Resonance Imaging (3DMRI) hippocampal and entorhinal cortex, and Standardized Uptake Value Ratio (SUVR) derived from Positron Emission Tomography (PET) images. Methods: To achieve this objective, we employed four distinct classifiers (Linear Support Vector Classification (L-SVM), Linear Discriminant Analysis (LDA), Logistic Regression (LR), and Logistic Regression Classifier with Stochastic Gradient Descent Learning (LRSGD)). These classifiers were used to derive both average and top-ranked importance scores for each feature based on their outputs. Our framework is designed to distinguish between two classes AD-negative (or mild cognitive impairment stable (MCIs): MCI stable) and AD-positive (or MCIc: MCI conversion) classes using a Probabilistic Neural Network (PNN) classifier and the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Results: The findings from the feature importance highlight the crucial role of the GLCM texture features extracted from the hippocampus and entorhinal cortex, demonstrating their superior performance compared to the volume and SUVR. GLCM-texture AD classification achieved approximately 90% sensitivity in identifying MCIc cases while maintaining low false positives (below 30%) when fused with other features. Moreover, the Receiver Operating Characteristic (ROC) curves validate the GLCMs’ superior performance in distinguishing between MCIs and MCIc. Additionally, fusing different types of features improved classification performance compared to relying solely on any single feature category. Conclusion: Our study emphasizes the pivotal role of GLCM texture features in early Alzheimer's detection.