Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the leading cause of dementia globally. Early prediction, prior to the onset of symptoms, is critical for enabling timely interventions. We present a machine learning framework that predicts future AD conversion in cognitively normal (CN) individuals using only structural magnetic resonance imaging (MRI) data. The approach leverages transfer learning with a pre-trained VGG16 model for feature extraction and processes five representative 2D slices per brain MRI scan to generate compact imaging descriptors. These features are classified using an ensemble composed of support vector machines (SVM), random forests (RF), and artificial neural networks (ANN), with outputs combined through soft voting. The model was evaluated using person-wise stratified cross-validation on 1,093 subjects from the OASIS-3 dataset, ensuring no data leakage and providing realistic performance estimates. Across 200 randomized runs, the ensemble achieved a median AUC-ROC of 0.951, accuracy of 0.872, recall of 0.923, and F1 score of 0.811. These results demonstrate that ensemble machine learning can detect preclinical AD signatures from structural MRI, offering a practical, relatively accessible, and cost-effective tool for early risk identification and intervention.
Theocharidis et al. (Tue,) studied this question.