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Trajectory Data Analyses for Pedestrian Space-time Activity Study

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A Data-Driven Approach to Predicting Recreational Activity Participation Using Machine Learning.

Seungbak Lee¹, Minsoo Kang¹

¹University of Mississippi.

Research Quarterly for Exercise and Sport

|June 14, 2024

Summary

This summary is machine-generated.

Machine learning models can predict recreational activity participation. Education level and moderate-vigorous physical activity are key factors influencing engagement in these activities.

Keywords:

Algorithms CRISP-DM permutation feature importance prediction model

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Area of Science:

Public Health
Data Science
Behavioral Science

Background:

Recreational activities are increasingly popular, yet understanding participation drivers is crucial.
Developing accurate prediction models for recreational activity engagement is a growing area of interest.
Identifying key factors influencing participation can inform public health initiatives.

Purpose of the Study:

To develop and compare machine learning models for predicting recreational activity participation.
To identify the most influential factors affecting engagement in recreational activities.
To enhance the interpretability of machine learning models in recreational research.

Main Methods:

Utilized data from 12,712 participants (aged 20+) from the National Health and Nutrition Examination Survey (NHANES) (2011-2018).
Developed 42 prediction models using six machine learning algorithms: logistic regression, SVM, decision tree, random forest, XGBoost, and LightGBM.
Assessed variable importance using permutation feature importance on top-performing models.

Main Results:

LightGBM demonstrated superior performance in predicting recreational activity participation (accuracy: .838, F1-score: .865).
Combining demographic and lifestyle data significantly improved prediction accuracy.
Education level and moderate-vigorous physical activity (MVPA) were identified as critical predictors.

Conclusions:

Machine learning offers a powerful data-driven approach for understanding and predicting recreational activity participation.
Feature importance analysis enhances the interpretability of complex machine learning models in this domain.
Findings highlight the importance of education and physical activity levels in promoting recreational engagement.