MRI Patterns of the Hippocampus and Amygdala for Predicting Stages of Alzheimer's Progression: A Minimal Feature Machine Learning Framework

TL;DR

This paper introduces a minimal-feature machine learning framework using MRI data of the hippocampus and amygdala to accurately classify stages of Alzheimer's disease progression, aiding early diagnosis and treatment planning.

Contribution

It presents a novel minimal-feature approach combining feature selection and dimensionality reduction to improve classification accuracy of Alzheimer's stages from MRI data.

Findings

Achieved up to 88.46% classification accuracy.

Effectively distinguished between EMCI, LMCI, and AD stages.

Reduced noise and dimensionality for better model performance.

Abstract

Alzheimer's disease (AD) progresses through distinct stages, from early mild cognitive impairment (EMCI) to late mild cognitive impairment (LMCI) and eventually to AD. Accurate identification of these stages, especially distinguishing LMCI from EMCI, is crucial for developing pre-dementia treatments but remains challenging due to subtle and overlapping imaging features. This study proposes a minimal-feature machine learning framework that leverages structural MRI data, focusing on the hippocampus and amygdala as regions of interest. The framework addresses the curse of dimensionality through feature selection, utilizes region-specific voxel information, and implements innovative data organization to enhance classification performance by reducing noise. The methodology integrates dimensionality reduction techniques such as PCA and t-SNE with state-of-the-art classifiers, achieving the highest accuracy of 88.46%. This framework demonstrates the potential for efficient and accurate staging of AD progression while providing valuable insights for clinical applications.