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

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