A multi-view classification framework for falls prediction: multiple-domain assessments in Parkinson's disease

Falls are one of the most common causes of injury and disability in people with Parkinson's disease (PD). This study developed an augmented machine learning framework for screening the risk of falling in people with PD using multiple domain assessments. A sample of 109 people with PD (50 fallers and 59 non-fallers) undertook four domains of assessment: disease-specific rating scales, clinical examination measures, physiological assessments, and gait analysis. A multi-view classifying framework was developed from a sequence of procedures and achieved 77.50% average predicting accuracy. The robustness of the multi-view framework was tested by comparing outcomes of three different view selection methods. The developed framework may have implications for clinical decision making, as some of the PD fall risk variables/features may be amenable to treatment. Our results showed that external reliability can be achieved by a simple voting mechanism from multiple, perhaps diverse, perspective consensus.