A Multi-Objective Approach to Predicting Motor and Cognitive Deficit in Parkinson's Disease Patients

Parkinson's disease (PD) is a chronic neurodegenerative condition. Traditionally categorised as a movement disorder, nowadays it is recognised that PD can also lead to significant cognitive dysfunction including, in many cases, full-blown dementia. Due to the wide range of symptoms, including significant overlap with other neurodegenerative conditions, both diagnosis and prognosis remain challenging. In this paper, we describe our use of a multi-objective evolutionary algorithm to explore trade-offs between polynomial regression models that predict different clinical measures, with the aim of identifying features that are most indicative of motor and cognitive PD variants. Our initial results are promising, showing that polynomial regression models are able to predict clinical measures with good accuracy, and that suitable predictive features can be identified.