Prediction of Alzheimer Disease on the DARWIN Dataset with Dimensionality Reduction and Explainability Techniques

Alexandre Moreira, Artur Ferreira, Artur Ferreira, Nuno Leite

2024

Abstract

The progressive degeneration of nerve cells causes neurodegenerative diseases. For instance, Alzheimer and Parkinson diseases progressively decrease the cognitive abilities and the motor skills of an individual. Without the knowledge for a cure, we aim to slow down their impact by resorting to rehabilitative therapies and medicines. Thus, early diagnosis plays a key role to delay the progression of these diseases. The analysis of handwriting dynamics for specific tasks is found to be an effective tool to provide early diagnosis of these diseases. Recently, the Diagnosis AlzheimeR WIth haNdwriting (DARWIN) dataset was introduced. It contains records of handwriting samples from 174 participants (diagnosed as having Alzheimer’s or not), performing 25 specific handwriting tasks, including dictation, graphics, and copies. In this paper, we explore the use of the DARWIN dataset with dimensionality reduction, explainability, and classification techniques. We identify the most relevant and decisive handwriting features for predicting Alzheimer. From the original set of 450 features with different groups, we found small subsets of features showing that the time spent to perform the in-air movements are the most decisive type of features for predicting Alzheimer.

Paper Citation

in Harvard Style

Moreira A., Ferreira A. and Leite N. (2024). Prediction of Alzheimer Disease on the DARWIN Dataset with Dimensionality Reduction and Explainability Techniques. In Proceedings of the 1st International Conference on Explainable AI for Neural and Symbolic Methods - Volume 1: EXPLAINS; ISBN 978-989-758-720-7, SciTePress, pages 38-49. DOI: 10.5220/0013017400003886

in Bibtex Style

@conference{explains24,
author={Alexandre Moreira and Artur Ferreira and Nuno Leite},
title={Prediction of Alzheimer Disease on the DARWIN Dataset with Dimensionality Reduction and Explainability Techniques},
booktitle={Proceedings of the 1st International Conference on Explainable AI for Neural and Symbolic Methods - Volume 1: EXPLAINS},
year={2024},
pages={38-49},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013017400003886},
isbn={978-989-758-720-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Explainable AI for Neural and Symbolic Methods - Volume 1: EXPLAINS
TI - Prediction of Alzheimer Disease on the DARWIN Dataset with Dimensionality Reduction and Explainability Techniques
SN - 978-989-758-720-7
AU - Moreira A.
AU - Ferreira A.
AU - Leite N.
PY - 2024
SP - 38
EP - 49
DO - 10.5220/0013017400003886
PB - SciTePress