Belayat Hossaina, Takatoshi Morookab, Makiko Okunob, Manabu Niia, Shinichi Yoshiyab, Syoji Kobashia
Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 105-115, 2019, DOI:10.31209/2018.100000034
Abstract This work aimed to predict postoperative knee functions of a new patient prior to
total knee arthroplasty (TKA) surgery using machine learning, because such
prediction is essential for surgical planning and for patients to better understand
the TKA outcome. However, the main difficulty is to determine the relationships
among individual varieties of preoperative and postoperative knee kinematics.
The problem was solved by constructing predictive models from the knee
kinematics data of 35 osteoarthritis patients, operated by posterior stabilized
implant, based on generalized linear regression (GLR) analysis. Two prediction
methods (without and with principal component analysis followed by GLR) along
with… More >
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