An improved sex-specific and age-dependent classification model for Parkinson's diagnosis using handwriting measurement. (June 2020)

• Record Type: Journal Article
• Authors: Gupta, Ujjwal; Bansal, Hritik; Joshi, Deepak
• Is Part Of: Computer methods and programs in biomedicine. Volume 189(2020)
• Publication Date: 2020-06
• Subjects: Parkinson's Disease' -- Sex-specific -- Age-dependent -- Handwriting Features -- Support Vector Machine; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2019.105305
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A sex-specific and age-dependent model for Parkison's diagnosis. Male and female specific features are discussed. Handwriting based markers for Parkinson's identification. Abstract: Background and Objectives: Diagnosis of Parkinson's with higher accuracy is always desirable to slow down the progression of the disease and improved quality of life. There are evidences of inherent neurological differences between male and females as well as between elderly and adults. However, the potential of such gender and age infomration have not been exploited yet for Parkinson's identification. Methods: 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 ± 10.9yrs) and healthy controls ( n = 38; m/f-20/18;age-62.4 ± 11.3yrs). A support vector machine ranking method is used to present the features specific to their dominance in sex and age group for Parkinson's diagnosis. Results: The sex-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. Conclusions: Combining the age and sex information proved to be encouraging in classification. A distinct set of features were observed to be dominating for higher classification accuracy in a different category of classification.