Predicting Alzheimer's Disease onset: A machine learning framework for early diagnosis using biomarker data

Abstract

Alzheimer’s Disease (AD) is a significant global health issue, and the current diagnostic techniques cannot diagnose the disease at its early stages, hence the difficulty of early therapeutic management. In response to the formulated research problem, this study articulates a new multimodal machine-learning framework for early AD diagnosis. The main goal is to combine multiple biomarkers: neuroimaging, CSF, genetic, and longitudinal cognitive data and develop a robust model for accurate early AD diagnosis. The importance of this work is in the opportunity to shift diagnostic paradigms by employing deep learning algorithms, including CNNs, LSTM networks, and GNNs to analyze spatial, temporal, and relational patterns across multi-modal data. The methodology involves federated learning and domain adaptation with GANs to integrate data from multiple centers with the patient’s privacy intact. It shows that the proposed multimodal model is superior to single-modality models with an AUC-ROC of 0.94 and reveals that hippocampal volume and plasma p-tau are the most informative biomarkers in the early diagnosis of AD. The study’s implications indicate that combining multimodal data improves diagnostic accuracy and clinical relevance by providing a roadmap to developing personalized medicine and better patient care. Future work will be aimed at increasing the variability of the dataset and the clinical trials to test the model to improve its applicability and performance in actual practice.