A Multi-Objective Approach to Predicting Motor and Cognitive Deficit in Parkinson's Disease Patients
作者： Marta Vallejo , Jeremy Cosgrove , Jane E. Alty , Stuart Jamieson , Stephen L. Smith
关键词: Polynomial regression 、 Parkinson's disease 、 Objective approach 、 Machine learning 、 Artificial intelligence 、 Medicine 、 Predictive modelling 、 Disease 、 Cognitive deficit 、 Dementia 、 Physical medicine and rehabilitation 、 Cognition
摘要: Parkinson's disease (PD) is a chronic neurodegenerative condition. Traditionally categorised as movement disorder, nowadays it recognised that PD can also lead to significant cognitive dysfunction including, in many cases, full-blown dementia. Due the wide range of symptoms, including overlap with other conditions, both diagnosis and prognosis remain challenging. In this paper, we describe our use multi-objective evolutionary algorithm explore trade-offs between polynomial regression models predict different clinical measures, aim identifying features are most indicative motor variants. Our initial results promising, showing able measures good accuracy, suitable predictive be identified.
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