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Machine learning discrimination of Parkinson's Disease stages from walker-mounted sensors data
A large set of features, some unique to this study are extracted and three feature selection methods are compared using a multi-class Random Forest (RF) classifier.
Automated machine vision enabled detection of movement disorders from hand drawn spirals
The discrimination accuracy of PD from controls was 98. 2%.
Estimation of Body Mass Index from Photographs using Deep Convolutional Neural Networks
Obesity is an important concern in public health, and Body Mass Index is one of the useful (and proliferant) measures.
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