Classifications based on response times for detecting early-stage Alzheimer's disease

TL;DR

This paper introduces a novel classification method using response times from handwriting and drawing tasks to detect early-stage Alzheimer's with high accuracy, outperforming existing methods and emphasizing simplicity and generalization.

Contribution

The paper proposes a new approach combining SVM feature selection, Gaussian mixture modeling, and a simple ad hoc classifier for early Alzheimer's detection.

Findings

Achieves 2-4 times fewer errors than state-of-the-art methods.

Uses a minimal set of features for high accuracy.

Proposes a simple classifier with good generalization potential.

Abstract

Introduction- This paper mainly describes a way to detect with high accuracy patients with early-stage Alzheimer's disease (ES-AD) versus healthy control (HC) subjects, from datasets built with handwriting and drawing task records. Method- The proposed approach uses subject's response times. An optimal subset of tasks is first selected with a "Support Vector Machine" (SVM) associated with a grid search. Mixtures of Gaussian distributions defined in the space of task durations are then used to reproduce and explain the results of the SVM. Finally, a surprisingly simple and efficient ad hoc classification algorithm is deduced from the Gaussian mixtures. Results- The solution presented in this paper makes two or even four times fewer errors than the best results of the state of the art concerning the classification HC/ES-AD from handwriting and drawing tasks. Discussion- The best SVM learning model reaches a high accuracy for this classification but its learning capacity is too large to ensure a low overfitting risk regarding the small size of the dataset. The proposed ad hoc classification algorithm only requires to optimize three real-parameters. It should therefore benefit from a good generalization ability.