Abstract
Supervised learning is the most common form of machine learning utilized in medical research. It is used to predict outcomes of interest or classify positive and/or negative cases with a known ground truth. Supervised learning describes a spectrum of techniques, ranging from traditional regression modeling to more complex tree boosting, which are becoming increasingly prevalent as the focus on “big data” develops. While these tools are becoming increasingly popular and powerful, there is a paucity of literature available that describe the strengths and limitations of these different modeling techniques. Typically, there is no formal training for health care professionals in the use of machine learning models. As machine learning applications throughout medicine increase, it is important that physicians and other health care professionals better understand the processes underlying application of these techniques. The purpose of this study is to provide an overview of commonly used supervised learning techniques with recent case examples within the orthopedic literature. An additional goal is to address disparities in the understanding of these methods to improve communication within and between research teams.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Anghel A, Papandreou N, Parnell T, et al (2018) Benchmarking and Optimization of Gradient Boosting Decision Tree Algorithms. Paper presented at NeurIPS 2018, IBM Research
Beam AL, Kohane IS (2018) Big Data and Machine Learning in Health Care. JAMA 319:1317–1318
Chen T, Guestrin C (2016) XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, 2016
Christodoulou E, Ma J, Collins GS et al (2019) A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J Clin Epidemiol 110:12–22
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297
Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 29:1189–1232
Géron A (2019) Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: concepts, tools, and techniques to build intelligent systems. O’Reilly Media Inc, Sebastopol, CA
Gravesteijn BY, Nieboer D, Ercole A et al (2020) Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury. J Clin Epidemiol 122:95–107
Hancock JT, Khoshgoftaar TM (2020) CatBoost for big data: an interdisciplinary review. J Big Data. https://doi.org/10.1186/s40537-020-00369-8
James G, Witten D, Hastie T et al (2021) An Introduction to Statistical Learning: with Applications in R. Springer Science + Business Media LLC, New York, NY
Jiang T, Gradus JL, Rosellini AJ (2020) Supervised machine learning: a brief primer. Behav Ther 51(5):675–687
Jurgensmeier K, Till SE, Lu Y et al (2022) Risk factors for secondary meniscus tears can be accurately predicted through machine learning, creating a resource for patient education and intervention. Knee Surg Sports Traumatol Arthrosc. https://doi.org/10.1007/s00167-022-07117-w
Ke G, Meng Q, Finley T et al. LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Paper presented at NeurIPS 2017, Microsoft Research,
Kotti M, Duffell LD, Faisal AA et al (2017) Detecting knee osteoarthritis and its discriminating parameters using random forests. Med Eng Phys 43:19–29
Ley C, Martin RK, Pareek A et al (2022) Machine learning and conventional statistics: making sense of the differences. Knee Surg Sports Traumatol Arthrosc. https://doi.org/10.1007/s00167-022-06896-6
Liew BXW, Kovacs FM, Rügamer D et al (2022) Machine learning versus logistic regression for prognostic modelling in individuals with non-specific neck pain. EuroSpine J 31(8):2082–2091
Lu Y, Pareek A, Lavoie-Gagne OZ et al (2022) Machine learning for predicting lower extremity muscle strain in National Basketball Association Athletes. Orthop J Sports Med 10(7):23259671221111744
Luu BC, Wright AL, Haeberle HS et al (2020) Machine learning outperforms logistic regression analysis to predict next-season NHL player injury: an analysis of 2322 players from 2007 to 2017. Orthop J Sports Med 8(9):2325967120953404
Mitchell T (1997) Machine learning. McGraw-Hill Education, New York, NY
Muller A, Guido S (2016) Introduction to machine learning with Python: a guide for Data Scientists. O’Reilly Media Inc., Sebastopol, CA
Nicholson KF, Collins GS, Waterman BR et al (2022) Machine learning and statistical prediction of pitching arm kinetics. Am J Sports Med 50:238–247
Nwachukwu BU, Beck EC, Lee EK et al (2020) Application of machine learning for predicting clinically meaningful outcome after arthroscopic femoroacetabular impingement surgery. Am J Sports Med 48:415–423
Prokhorenkova L, Gusev G, Vorobev A et al (2017) CatBoost: unbiased boosting with categorical features. Paper presented at NeurIPS 2018, Yandex
Ramkumar PN, Karnuta JM, Haeberle HS et al (2021) Association between preoperative mental health and clinically meaningful outcomes after osteochondral allograft for cartilage defects of the knee: a machine learning analysis. Am J Sports Med 49:948–957
Ramkumar PN, Karnuta JM, Navarro SM et al (2019) Preoperative prediction of value metrics and a patient-specific payment model for primary total hip arthroplasty: development and validation of a deep learning model. J Arthroplasty 34:2228-2234.e1
Ramkumar PN, Pang M, Polisetty T et al (2022) Meaningless applications and misguided methodologies in artificial intelligence–related orthopaedic research propagates hype over hope. Arthroscopy. https://doi.org/10.1016/j.arthro.2022.04.014
Silver D, Schrittwieser J, Simonyan K et al (2017) Mastering the game of Go without human knowledge. Nature 550:354–359
Singh A, Thakur N, Sharma A (2016) A review of supervised machine learning algorithms. Paper presented at the 3rd International Conference on Computing for Sustainable Global Development (INDIACom), Bharati Vidyapeeth's College of Engineering, 16–18 March 2016
Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14:199–222
Synced (2017) Tree Boosting With XGBoost – Why Does XGBoost Win “Every” Machine Learning Competition? https://syncedreview.com/2017/10/22/tree-boosting-with-xgboost-why-does-xgboost-win-every-machine-learning-competition. Accessed 17 Aug 2022
Whiteside D, Martini DN, Lepley AS et al (2016) Predictors of ulnar collateral ligament reconstruction in Major League Baseball pitchers. Am J Sports Med 44:2202–2209
Funding
The authors received no funding for this study.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no conflict of interest with regards to this publication.
Ethical approval
There was no ethical approval required for this study.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Pruneski, J.A., Pareek, A., Kunze, K.N. et al. Supervised machine learning and associated algorithms: applications in orthopedic surgery. Knee Surg Sports Traumatol Arthrosc 31, 1196–1202 (2023). https://doi.org/10.1007/s00167-022-07181-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00167-022-07181-2