  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.
