The ensemble machine learning prediction approach leverages generalized linear model, random forest, and partial least squares. The current work can further be improvised to predict other health parameters and recommend corrective measures based on obesity values....
Mehmet Gülü, et al., “Exploring Obesity, Physical Activity, and Digital Game Addiction Levels among Adolescents: A Study on Machine Learning-Based Prediction of Digital Game Addiction,” Frontiers in Psychology, frontiersin.org, Mar. 2, 2023 ...
Another robust ensemble-based ML method is Gradient Boosting Machines (GBM)11. In this case, the decision trees are added sequentially, where one tree is fitted to reduce the prediction error of the previous ones. Normally a stochastic version of this approach is used, using at each new added...
alexdsbreslav / slot_machine_two_step_task Star 1 Code Issues Pull requests [In Production] Adaptation of Nathaniel Daw's Two-Step Sequential Learning Task. Designed for a study of reward prediction for food with college undergraduates. learning matlab decision-making eye-tracking cognitive-scien...
A PIONEERING study led by researchers from Lund University Diabetes Centre and international partners has introduced a precision prediction algorithm that can detect subtypes of obesity, significantly improving the prediction of obesity-related health risks. The study shed light on how diverse ob...
The aim of this research is to develop an artificial neural network based system for childhood obesity prediction based on various types of medical and sociological data. If obesity could be predicted it could be prevented in most cases and degenerative and chronic diseases caused by it avoided. ...
Obesity Related Disease Prediction from Healthcare Communities Using Machine Learning Moreover, the underlying problem that persists is not only identifying obesity as a physical medical problem but also speculating the different factors that ... NC Pereira,J D'Souza,P Rana,... 被引量: 0发表: ...
In addition, H-NMR metabolomics improved the prediction of future cardiometabolic disease in comparison with models relying on just anthropometric measures. Conclusions This study demonstrated the benefits of using precision techniques like H-NMR to better assess the risk of obesity-derived cardiometabolic...
This CNN will allow us to associate land use patterns with obesity trends and lend greater interpretability to prediction models. However, we were unable to find openly available networks pretrained on aerial and satellite imagery data sets for such a task. Google Street View images are a ...
Machine learning approach for the early prediction of the risk of overweight and obesity in young people. International Conference on Computational Science; Springer, Cham; 2020: 523–535. 60. Alotaibi M. A social robotic obesity management and awareness system for children in Saudi Arabia. Int ...