An Ensemble Learning Approach Based on Diffusion Tensor Imaging Measures for Alzheimer’s Disease Classification

Abstract

Recent advances in neuroimaging techniques, such as diffusion tensor imaging (DTI), represent a crucial resource for structural brain analysis and allow the identification of alterations related to severe neurodegenerative disorders, such as Alzheimer’s disease (AD). At the same time, machine-learning-based computational tools for early diagnosis and decision support systems are adopted to uncover hidden patterns in data for phenotype stratification and to identify pathological scenarios. In this landscape, ensemble learning approaches, conceived to simulate human behavior in making decisions, are suitable methods in healthcare prediction tasks, generally improving classification performances. In this work, we propose a novel technique for the automatic discrimination between healthy controls and AD patients, using DTI measures as predicting features and a soft-voting ensemble approach for the classification. We show that this approach, efficiently combining single classifiers trained on specific groups of features, is able to improve classification performances with respect to the comprehensive approach of the concatenation of global features (with an increase of up to 9% on average) and the use of individual groups of features (with a notable enhancement in sensitivity of up to 11%). Ultimately, the feature selection phase in similar classification tasks can take advantage of this kind of strategy, allowing one to exploit the information content of data and at the same time reducing the dimensionality of the feature space, and in turn the computational effort.

Multimodal magnetic resonance imaging for Alzheimer's disease diagnosis using hybrid features extraction and ensemble support vector machines

“…17 Maggipinto et al 18 used TBSS to generate the WM skeleton from FA and MD to build a Random Forest classifier (RF). Similarly, Lella et al 19 developed an ensemble machine learning approach using the WM skeleton information of the four-diffusivity metrics, FA, MD, radial diffusivity (RD), and longitudinal diffusivity (LD), to classify CN versus AD. This information was utilized to train three classifiers: SVM, RF, and Multi-layer perceptron (MLP).…”

Automated detection of Alzheimer’s disease: a multi-modal approach with 3D MRI and amyloid PET

“…MRI, utilizing magnetic fields and radio waves, generates high-quality two- or three-dimensional images of brain structures without requiring X-rays or radioactive tracers. This technology has significantly contributed to the development of diagnostic models for AD, offering a non-invasive method to detect patterns of brain atrophy indicative of the disease 2 – 4 .…”

Novel Computer-Aided Diagnosis System for the Early Detection of Alzheimer’s Disease

“…A RF-based feature selection model featuring a Gaussian-inspired algorithm achieved a higher classification accuracy of 78.8% over the SVM classifier and a classification accuracy of 75.6% for determining between patients with CN vs. EMCI [12]. Ensemble learning approaches featuring soft-voting classification are a reliable method in healthcare-related prediction tasks, especially for enhancing classification results [13]. The hybrid feature selection framework (using the significance analysis of microarray (SAM) filter) identifies the most deterministic biomarkers for AD detection and achieved an accuracy of 87% [14].…”