Class Balancing Diversity Multimodal Ensemble for Alzheimer's Disease Diagnosis and Early Detection

TL;DR

This paper introduces IMBALMED, a novel multimodal ensemble approach with class balancing techniques that significantly improves early detection and diagnosis of Alzheimer's disease using diverse data sources.

Contribution

It presents a new ensemble method that integrates multimodal data and class balancing to enhance early AD detection and address data imbalance issues.

Findings

IMBALMED outperforms state-of-the-art algorithms in accuracy.

It significantly improves early MCI detection at 48 months.

The approach demonstrates robustness across multiple classification tasks.

Abstract

Alzheimer's disease (AD) poses significant global health challenges due to its increasing prevalence and associated societal costs. Early detection and diagnosis of AD are critical for delaying progression and improving patient outcomes. Traditional diagnostic methods and single-modality data often fall short in identifying early-stage AD and distinguishing it from Mild Cognitive Impairment (MCI). This study addresses these challenges by introducing a novel approach: multImodal enseMble via class BALancing diversity for iMbalancEd Data (IMBALMED). IMBALMED integrates multimodal data from the Alzheimer's Disease Neuroimaging Initiative database, including clinical assessments, neuroimaging phenotypes, biospecimen and subject characteristics data. It employs an ensemble of model classifiers, each trained with different class balancing techniques, to overcome class imbalance and enhance model accuracy. We evaluate IMBALMED on two diagnostic tasks (binary and ternary classification) and four binary early detection tasks (at 12, 24, 36, and 48 months), comparing its performance with state-of-the-art algorithms and an unbalanced dataset method. IMBALMED demonstrates superior diagnostic accuracy and predictive performance in both binary and ternary classification tasks, significantly improving early detection of MCI at 48-month time point. The method shows improved classification performance and robustness, offering a promising solution for early detection and management of AD.