In this section
@article{Kotti:2017:10.1016/j.medengphy.2017.02.004,
author = {Kotti, M and Duffell, LD and Faisal, AA and McGregor, AH},
doi = {10.1016/j.medengphy.2017.02.004},
journal = {Medical Engineering and Physics},
pages = {19--29},
title = {Detecting knee osteoarthritis and its discriminating parameters using random forests},
url = {http://dx.doi.org/10.1016/j.medengphy.2017.02.004},
volume = {43},
year = {2017}
}
TY - JOUR
AB - This papertackles the problem of automatic detection of knee osteoarthritis. A computer system is built that takes as input the body kinetics and produces as output not only an estimation of presence of the knee osteoarthritis,as previouslydone inthe literature, but alsothe most discriminating parameters along with a set of rules on how this decision was reached.This fills the gap of interpretability between the medical and the engineering approaches. We collected locomotion data from 47 subjects with knee osteoarthritis and 47 healthy subjects.Osteoarthritis subjects were recruited from hospital clinics and GP surgeries, and age and sex matched heathy subjects from the local community. Subjects walked on a walkway equippedwith two force plates with piezoelectric 3-component force sensors. Parameters of the vertical, anterior-posterior, and medio-lateral ground reaction forces, such asmean value, push-off time, and slope,were extracted. Then random forest regressors map thoseparameters via rule induction to the degree of knee osteoarthritis.To boost generalisation ability,a subject-independent protocol is employed.The 5-fold cross-validated accuracy is 72.61%±4.24%. We show that with 3 steps or lessa reliable clinical measure can be extractedin a rule-based approachwhen the dataset is analysed appropriately.
AU - Kotti,M
AU - Duffell,LD
AU - Faisal,AA
AU - McGregor,AH
DO - 10.1016/j.medengphy.2017.02.004
EP - 29
PY - 2017///
SN - 1350-4533
SP - 19
TI - Detecting knee osteoarthritis and its discriminating parameters using random forests
T2 - Medical Engineering and Physics
UR - http://dx.doi.org/10.1016/j.medengphy.2017.02.004
VL - 43
ER -