Gender specific and Age dependent classification model for improved diagnosis in Parkinson's disease

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 gender specific and age dependent classification method to diagnose the Parkinson's disease using the handwriting based measurements. The gender specific and age dependent classifier was observed significantly outperforming the generalized classifier. An improved accuracy of 83.75% (SD=1.63) with the female specific classifier, and 79.55% (SD=1.58) with the old age dependent classifier was observed in comparison to 75.76% (SD=1.17) accuracy with the generalized classifier. Finally, combining the age and gender information proved to be encouraging in classification. We performed a rigorous analysis to observe the dominance of gender 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.