Published on in Vol 23, No 7 (2021): July
Preprints (earlier versions) of this paper are
available at
https://preprints.jmir.org/preprint/22021, first published
.
Journals
- Raudeniece J, Vanags E, Justamente I, Skara D, Fredriksen P, Brownlee I, Reihmane D. Relations between the levels of moderate to vigorous physical activity, BMI, dietary habits, cognitive functions and attention problems in 8 to 9 years old pupils: network analysis (PACH Study). BMC Public Health 2024;24(1) View
- González-Carrasco M, Aciar S, Casas F, Oriol X, Fabregat R, Malo S. A Machine Learning Approach to Well-Being in Late Childhood and Early Adolescence: The Children’s Worlds Data Case. Social Indicators Research 2024;175(1):25 View
- Tan A, Ali F, Poon K. Subjective well‐being of children with special educational needs: Longitudinal predictors using machine learning. Applied Psychology: Health and Well-Being 2024 View
- Bouliotis G, Underwood M, Froud R. Predicting the time to get back to work using statistical models and machine learning approaches. BMC Medical Research Methodology 2024;24(1) View
- Khine M, Liu Y, Pallipuram V, Afari E. A Machine-Learning Approach to Predicting the Achievement of Australian Students Using School Climate; Learner Characteristics; and Economic, Social, and Cultural Status. Education Sciences 2024;14(12):1350 View
Books/Policy Documents
- Espinosa-Pinos C, Ayala-Chauvín I, Buele J. Technologies and Innovation. View