Classifying individuals with and without patellofemoral pain syndrome using ground force profiles - Development of a method using functional data boosting.
Biomechanics
Functional regression
Jumping
Machine learning
Patellofemoral pain syndrome
Journal
Gait & posture
ISSN: 1879-2219
Titre abrégé: Gait Posture
Pays: England
ID NLM: 9416830
Informations de publication
Date de publication:
07 2020
07 2020
Historique:
received:
04
03
2020
revised:
06
05
2020
accepted:
21
05
2020
pubmed:
5
6
2020
medline:
13
4
2021
entrez:
5
6
2020
Statut:
ppublish
Résumé
Predictors of recovery in patellofemoral pain syndrome (PFPS) currently used in prognostic models are scalar in nature, despite many physiological measures originally lying on the functional scale. Traditional modelling techniques cannot harness the potential predictive value of functional physiological variables. What is the classification performance of PFPS status of a statistical model when using functional ground reaction force (GRF) time-series? Thirty-one individuals (control = 17, PFPS = 14) performed maximal countermovement jumps, on two force plates. The three-dimensional components of the GRF profiles were time-normalized between the start of the eccentric phase and take-off, and used as functional predictors. A statistical model was developed using functional data boosting (FDboost), for binary classification of PFPS statuses (control vs PFPS). The area under the Receiver Operating Characteristic curve (AUC) was used to quantify the model's ability to discriminate the two groups. The three predictors of GRF waveform achieved an average out-of-bag AUC of 93.7 %. A 1 % increase in applied medial force reduced the log odds of being in the PFPS group by 0.68 at 87 % of jump cycle. In the AP direction, a 1 % reduction in applied posterior force increased the log odds of being classified as PFPS by 1.10 at 70 % jump cycle. For the vertical GRF, a 1 % increase in applied force reduced the log odds of being classified in the PFPS group by 0.12 at 44 % of the jump cycle. Using simple functional GRF variables collected during functionally relevant task, in conjunction with FDboost, produced clinically interpretable models that retain excellent classification performance in individuals with PFPS. FDboost may be an invaluable tool to be used in longitudinal cohort prognostic studies, especially when scalar and functional predictors are collected.
Sections du résumé
BACKGROUND
Predictors of recovery in patellofemoral pain syndrome (PFPS) currently used in prognostic models are scalar in nature, despite many physiological measures originally lying on the functional scale. Traditional modelling techniques cannot harness the potential predictive value of functional physiological variables.
RESEARCH QUESTION
What is the classification performance of PFPS status of a statistical model when using functional ground reaction force (GRF) time-series?
METHODS
Thirty-one individuals (control = 17, PFPS = 14) performed maximal countermovement jumps, on two force plates. The three-dimensional components of the GRF profiles were time-normalized between the start of the eccentric phase and take-off, and used as functional predictors. A statistical model was developed using functional data boosting (FDboost), for binary classification of PFPS statuses (control vs PFPS). The area under the Receiver Operating Characteristic curve (AUC) was used to quantify the model's ability to discriminate the two groups.
RESULTS
The three predictors of GRF waveform achieved an average out-of-bag AUC of 93.7 %. A 1 % increase in applied medial force reduced the log odds of being in the PFPS group by 0.68 at 87 % of jump cycle. In the AP direction, a 1 % reduction in applied posterior force increased the log odds of being classified as PFPS by 1.10 at 70 % jump cycle. For the vertical GRF, a 1 % increase in applied force reduced the log odds of being classified in the PFPS group by 0.12 at 44 % of the jump cycle.
SIGNIFICANCE
Using simple functional GRF variables collected during functionally relevant task, in conjunction with FDboost, produced clinically interpretable models that retain excellent classification performance in individuals with PFPS. FDboost may be an invaluable tool to be used in longitudinal cohort prognostic studies, especially when scalar and functional predictors are collected.
Identifiants
pubmed: 32497981
pii: S0966-6362(20)30184-3
doi: 10.1016/j.gaitpost.2020.05.034
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
90-95Informations de copyright
Copyright © 2020 Elsevier B.V. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of Competing Interest All authors declare that they have no conflicts of interest.