An improved sex specific and age dependent classification model for Parkinson's diagnosis using handwriting measurement

TL;DR

This study develops sex-specific and age-dependent handwriting-based classifiers for Parkinson's diagnosis, significantly improving accuracy over generalized models by leveraging demographic-specific features and rigorous feature ranking.

Contribution

The paper introduces a novel sex-specific and age-dependent classification approach for Parkinson's diagnosis using handwriting data, outperforming generalized models.

Findings

Female-specific classifier achieved 83.75% accuracy.

Age-dependent classifier achieved 79.55% accuracy.

Combining age and sex information further improved classification.

Abstract

Accurate diagnosis is crucial for preventing the progression of Parkinson's, as well as improving the quality of life with individuals with Parkinson's disease. In this paper, we develop a sex-specific and age-dependent classification method to diagnose the Parkinson's disease using the online handwriting recorded from individuals with Parkinson's(n=37;m/f-19/18;age-69.3+-10.9years) and healthy controls(n=38;m/f-20/18;age-62.4+-11.3 years).The sex specific and age dependent classifier was observed significantly outperforming the generalized classifier. An improved accuracy of 83.75%(SD+1.63) with female specific classifier, and 79.55%(SD=1.58) with old age dependent classifier was observed in comparison to 75.76%(SD=1.17) accuracy with the generalized classifier. Finally, combining the age and sex information proved to be encouraging in classification. We performed a rigorous analysis to observe the dominance of sex specific and age dependent features for Parkinson's detection and ranked them using the support vector machine(SVM) ranking method. Distinct set of features were observed to be dominating for higher classification accuracy in different category of classification.