Analysis of the influence of sex in diagnostic classification of Parkinson's disease based on non-motor manifestations by means of machine learning methods

Abstract

Parkinson’s disease (PD) is the second most common neurodegenerative disorder, after Alzheimer’s disease. In the early stages of the disease, when motor symptoms have not yet manifested themselves, the accuracy of making a correct diagnosis is currently very limited. This work aims to analyse the influence of sex in diagnostic classification of Parkinson’s disease based on non-motor symptoms by using machine learning methods. These symptoms have been evaluated in 490 subjects with PD and 197 healthy control subjects.