An improved sex specific and age dependent classification model for Parkinson's diagnosis using handwriting measurement
This work addresses Parkinson's diagnosis for patients by improving classification accuracy through demographic-specific modeling, though it is incremental as it builds on existing methods with new data adaptations.
The paper tackled Parkinson's disease diagnosis by developing sex-specific and age-dependent classifiers using online handwriting data, achieving improved accuracies of 83.75% for female-specific and 79.55% for old age-dependent models compared to 75.76% for a generalized classifier.
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.