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Using Machine Learning Algorithms to Pool Data from Meta-Analysis for the Prediction of Countermovement Jump Improvement

Author

Listed:
  • Indy Man Kit Ho

    (Department of Sports and Recreation, Technological and Higher Education Institute of Hong Kong (THEi), Chai Wan, Hong Kong, China
    The Asian Academy for Sports and Fitness Professionals, Chai Wan, Hongkong, China)

  • Anthony Weldon

    (Centre for Life and Sport Sciences, Birmingham City University, Birmingham B15 3TN, UK)

  • Jason Tze Ho Yong

    (Department of Sports and Recreation, Technological and Higher Education Institute of Hong Kong (THEi), Chai Wan, Hong Kong, China)

  • Candy Tze Tim Lam

    (Department of Sports and Recreation, Technological and Higher Education Institute of Hong Kong (THEi), Chai Wan, Hong Kong, China)

  • Jaime Sampaio

    (Research Center in Sports Sciences, Health Sciences and Human Development, CIDESD, CreativeLab Research Community, 5000-801 Vila Real, Portugal)

Abstract

To solve the research–practice gap and take one step forward toward using big data with real-world evidence, the present study aims to adopt a novel method using machine learning to pool findings from meta-analyses and predict the change of countermovement jump. The data were collected through a total of 124 individual studies included in 16 recent meta-analyses. The performance of four selected machine learning algorithms including support vector machine, random forest (RF) ensemble, light gradient boosted machine, and the neural network using multi-layer perceptron was compared. The RF yielded the highest accuracy (mean absolute error: 0.071 cm; R 2 : 0.985). Based on the feature importance calculated by the RF regressor, the baseline CMJ (“Pre-CMJ”) was the most impactful predictor, followed by age (“Age”), the total number of training sessions received (“Total number of training_session”), controlled or non-controlled conditions (“Control (no training)”), whether the training program included squat, lunge, deadlift, or hip thrust exercises (“Squat_Lunge_Deadlift_Hipthrust_True”, “Squat_Lunge_Deadlift_Hipthrust_False”), or “Plyometric (mixed fast/slow SSC)”, and whether the athlete was from an Asian pacific region including Australia (“Race_Asian or Australian”). By using multiple simulated virtual cases, the successful predictions of the CMJ improvement are shown, whereas the perceived benefits and limitations of using machine learning in a meta-analysis are discussed.

Suggested Citation

  • Indy Man Kit Ho & Anthony Weldon & Jason Tze Ho Yong & Candy Tze Tim Lam & Jaime Sampaio, 2023. "Using Machine Learning Algorithms to Pool Data from Meta-Analysis for the Prediction of Countermovement Jump Improvement," IJERPH, MDPI, vol. 20(10), pages 1-15, May.
  • Handle: RePEc:gam:jijerp:v:20:y:2023:i:10:p:5881-:d:1151077
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    References listed on IDEAS

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