Logo image
Back
Machine learning methods are comparable to logistic regression techniques in predicting severe walking limitation following total knee arthroplasty
Journal article   Peer reviewed

Machine learning methods are comparable to logistic regression techniques in predicting severe walking limitation following total knee arthroplasty

Yong-Hao Pua, H Kang, J Thumboo, Ross Clark, E S Chew, C L Poon, H C Chong and S J Yeo
Knee Surgery, Sports Traumatology, Arthroscopy, Vol.28, pp.3207-3216
2020
DOI: https://doi.org/10.1007/s00167-019-05822-7
url
https://doi.org/10.1007/s00167-019-05822-7View
Published Version

Abstract

algorithms arthroplasty artificial intelligence knee machine learning prediction replacement
PURPOSE: Machine-learning methods are flexible prediction algorithms with potential advantages over conventional regression. This study aimed to use machine learning methods to predict post-total knee arthroplasty (TKA) walking limitation, and to compare their performance with that of logistic regression. METHODS: From the department's clinical registry, a cohort of 4026 patients who underwent elective, primary TKA between July 2013 and July 2017 was identified. Candidate predictors included demographics and preoperative clinical, psychosocial, and outcome measures. The primary outcome was severe walking limitation at 6 months post-TKA, defined as a maximum walk time ≤ 15 min. Eight common regression (logistic, penalized logistic, and ordinal logistic with natural splines) and ensemble machine learning (random forest, extreme gradient boosting, and SuperLearner) methods were implemented to predict the probability of severe walking limitation. Models were compared on discrimination and calibration metrics. RESULTS: At 6 months post-TKA, 13% of patients had severe walking limitation. Machine learning and logistic regression models performed moderately [mean area under the ROC curves (AUC) 0.73-0.75]. Overall, the ordinal logistic regression model performed best while the SuperLearner performed best among machine learning methods, with negligible differences between them (Brier score difference, < 0.001; 95% CI [- 0.0025, 0.002]). CONCLUSIONS: When predicting post-TKA physical function, several machine learning methods did not outperform logistic regression-in particular, ordinal logistic regression that does not assume linearity in its predictors.

Details

Metrics

89 Record Views
44 Times Cited - Web of Science

InCites Highlights

These are selected metrics from InCites Benchmarking & Analytics tool, related to this output

Collaboration types
Domestic collaboration
International collaboration
Web Of Science research areas
Orthopedics
Sport Sciences
Surgery

UN Sustainable Development Goals (SDGs)

This output has contributed to the advancement of the following goals:

#3 Good Health and Well-Being

Source: InCites

Logo image