How word semantics and phonology affect handwriting of Alzheimer's patients: a machine learning based analysis

TL;DR

This study uses machine learning to analyze how word meaning and phonology influence handwriting in Alzheimer's patients, revealing that non-regular words are most effective for classification with high accuracy.

Contribution

It introduces a machine learning approach to differentiate handwriting patterns based on word semantics and phonology in Alzheimer's disease, highlighting the importance of word type in analysis.

Findings

Non-regular words achieved nearly 90% classification accuracy.

Feature selection yielded distinct feature sets for each word type.

Non-regular words required more features but provided better classification results.

Abstract

Using kinematic properties of handwriting to support the diagnosis of neurodegenerative disease is a real challenge: non-invasive detection techniques combined with machine learning approaches promise big steps forward in this research field. In literature, the tasks proposed focused on different cognitive skills to elicitate handwriting movements. In particular, the meaning and phonology of words to copy can compromise writing fluency. In this paper, we investigated how word semantics and phonology affect the handwriting of people affected by Alzheimer's disease. To this aim, we used the data from six handwriting tasks, each requiring copying a word belonging to one of the following categories: regular (have a predictable phoneme-grapheme correspondence, e.g., cat), non-regular (have atypical phoneme-grapheme correspondence, e.g., laugh), and non-word (non-meaningful pronounceable letter strings that conform to phoneme-grapheme conversion rules). We analyzed the data using a machine learning approach by implementing four well-known and widely-used classifiers and feature selection. The experimental results showed that the feature selection allowed us to derive a different set of highly distinctive features for each word type. Furthermore, non-regular words needed, on average, more features but achieved excellent classification performance: the best result was obtained on a non-regular, reaching an accuracy close to 90%.