title: Application of Machine Learning in Postural Control Kinematics for the Diagnosis of Alzheimer's Disease.
abstract: The use of wearable devices to study gait and postural control is a growing field on neurodegenerative disorders such as Alzheimer's disease (AD). In this paper, we investigate if machine-learning classifiers offer the discriminative power for the diagnosis of AD based on postural control kinematics. We compared Support Vector Machines (SVMs), Multiple Layer Perceptrons (MLPs), Radial Basis Function Neural Networks (RBNs), and Deep Belief Networks (DBNs) on 72 participants (36 AD patients and 36 healthy subjects) exposed to seven increasingly difficult postural tasks. The decisional space was composed of 18 kinematic variables (adjusted for age, education, height, and weight), with or without neuropsychological evaluation (Montreal cognitive assessment (MoCA) score), top ranked in an error incremental analysis. Classification results were based on threefold cross validation of 50 independent and randomized runs sets: training (50%), test (40%), and validation (10%). Having a decisional space relying solely on postural kinematics, accuracy of AD diagnosis ranged from 71.7 to 86.1%. Adding the MoCA variable, the accuracy ranged between 91 and 96.6%. MLP classifier achieved top performance in both decisional spaces. Having comprehended the interdynamic interaction between postural stability and cognitive performance, our results endorse machine-learning models as a useful tool for computer-aided diagnosis of AD based on postural control kinematics.
authors.affiliation: Algoritmi Center, Department of Industrial Electronics, School of Engineering, University of Minho, Braga, Portugal.
authors.forename: Luís
authors.identifier: 0000-0002-6566-3927
authors.initials: L
authors.lastname: Costa
authors.affiliation: Neurology Department, Hospital da Senhora da Oliveira, Guimarães, Portugal; Life and Health Sciences Research Institute (ICVS), School of Health Sciences, University of Minho, Braga, Portugal.
authors.forename: Miguel F
authors.identifier: 0000-0003-0894-6207
authors.initials: MF
authors.lastname: Gago
authors.affiliation: Algoritmi Center, Department of Industrial Electronics, School of Engineering, University of Minho, Braga, Portugal.
authors.forename: Darya
authors.initials: D
authors.lastname: Yelshyna
authors.affiliation: Algoritmi Center, Department of Industrial Electronics, School of Engineering, University of Minho, Braga, Portugal.
authors.forename: Jaime
authors.identifier: 0000-0001-5355-3117
authors.initials: J
authors.lastname: Ferreira
authors.affiliation: Algoritmi Center, Department of Industrial Electronics, School of Engineering, University of Minho, Braga, Portugal.
authors.forename: Hélder
authors.identifier: 0000-0002-6570-6501
authors.initials: H
authors.lastname: David Silva
authors.affiliation: Algoritmi Center, Department of Industrial Electronics, School of Engineering, University of Minho, Braga, Portugal.
authors.forename: Luís
authors.identifier: 0000-0001-8184-8801
authors.initials: L
authors.lastname: Rocha
authors.affiliation: Life and Health Sciences Research Institute (ICVS), School of Health Sciences, University of Minho, Braga, Portugal; ICVS-3B's-PT Government Associate Laboratory, Braga, Portugal.
authors.forename: Nuno
authors.identifier: 0000-0002-8755-5126
authors.initials: N
authors.lastname: Sousa
authors.affiliation: Algoritmi Center, Department of Industrial Electronics, School of Engineering, University of Minho, Braga, Portugal.
authors.forename: Estela
authors.initials: E
authors.lastname: Bicho
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000150:complications / Q000175:diagnosis*; D001696:Biomechanical Phenomena / Q000502:physiology*; D003936:Diagnosis, Computer-Assisted; D005260:Female; D006801:Humans; D000069550:Machine Learning; D008297:Male; D016571:Neural Networks, Computer; D004856:Postural Balance / Q000502:physiology*; D011187:Posture; D011569:Psychiatric Status Rating Scales; D012680:Sensitivity and Specificity