000001098 001__ 1098
000001098 005__ 20190611210126.0
000001098 022__ $$a978-1-61499-511-1
000001098 037__ $$aCONFERENCE
000001098 041__ $$aeng
000001098 245__ $$aApplying machine learning to gait analysis data for disease identification
000001098 260__ $$c2015$$b27-29 May 2015$$aMadrid, Spain
000001098 269__ $$a2015-05
000001098 300__ $$a5 p.
000001098 506__ $$avisible
000001098 520__ $$aA machine-learning framework to identify the specific disease afflicting certain patients using only gait analysis data is presented. Classifying such data into disease types consumes valuable clinical time that may be better spent. Effective classification also facilitates its future retrieval. To prove the feasibility of the approach, we applied it to the simpler case of identifying the disease class of patients with a view to extending the method to specific diseases in future work. The patients benefiting from this framework suffer from Neurological and Neuromuscular Diseases (NND), or Juvenile Idiopathic Arthritis (JIA). Standard clinical gait information of healthy individuals, and NND/JIA patients was sourced from hospitals participating in MD-PAEDIGREE. To classify the data into one of the three categories: healthy, NND, and JIA, certain parameters were carefully selected from them and used to train Random Forest (RF), boosting, Multilayer Perceptron (MLP), and Support Vector Machine (SVM) classifiers. Cross-validation was used to test the effectiveness of our approach and it yields a classification accuracy of 100% for RF, SVM, and MLP classifiers and 96.4% for boosting. Training and testing for all the classifiers took mere milliseconds, providing opportunities for real-time applications. To extend the method to the identification of specific illnesses, more discerning features from the gait data are currently being investigated. Moreover, a larger dataset is being gathered. Finally, we are attempting to reduce the number of features used for classification in order to further decrease computation time and algorithm complexity.$$9eng
000001098 592__ $$aHEG-VS
000001098 592__ $$bInstitut Informatique de gestion
000001098 592__ $$cEconomie et Services
000001098 65017 $$aInformatique
000001098 65017 $$aAutres
000001098 655_7 $$afull paper
000001098 6531_ $$amedical informatics$$9eng
000001098 6531_ $$agait classification$$9eng
000001098 6531_ $$amachine learning$$9eng
000001098 6531_ $$asupport vector machines$$9eng
000001098 6531_ $$aneural networks$$9eng
000001098 700__ $$uUniversity of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)$$aMüller, Henning
000001098 700__ $$uETH, Zurich, Switzerland$$aJoyseeree, Ranveer
000001098 700__ $$aSabha, Rami Abou$$uUniversity of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)
000001098 711__ $$aMedical Informatics Europe (MIE)$$cMadrid, Spain$$d27/05/2015 / 29/05/2015
000001098 773__ $$tProocedings of the Medical Informatics Europe (MIE) 2015
000001098 8564_ $$uhttps://hesso.tind.io/record/1098/files/M%C3%BCller_ApplyingMachineLearningToGaitAnalysisData_2015.pdf$$s195768
000001098 906__ $$aNONE
000001098 950__ $$aI1
000001098 980__ $$aconference