Using Machine Learning Algorithms to Pool Data from Meta-Analysis for the Prediction of Countermovement Jump Improvement.
Indy Man Kit HoAnthony WeldonJason Tze Ho YongCandy Tze Tim LamJaime SampaioPublished in: International journal of environmental research and public health (2023)
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.
Keyphrases
- meta analyses
- machine learning
- big data
- neural network
- systematic review
- artificial intelligence
- deep learning
- randomized controlled trial
- virtual reality
- convolutional neural network
- healthcare
- primary care
- climate change
- social support
- total hip arthroplasty
- electronic health record
- quality improvement
- mental health
- transcranial direct current stimulation
- working memory