Predicting Dropouts From an Electronic Health Platform for Lifestyle Interventions: Analysis of Methods and Predictors.

Daniel Hansen Pedersen Marjan Mansourvar Camilla Sortsø Thomas Schmidt

Published in: Journal of medical Internet research (2019)

Dropouts from eHealth lifestyle interventions can be predicted using various data mining methods. This can support health coaches in preventing attrition by receiving proactive warnings. The best performing predictive model was found to be the random forest.

Keyphrases

physical activity
public health
healthcare
metabolic syndrome
mental health
cardiovascular disease
weight loss
health information
climate change
high throughput
big data
health promotion
machine learning
deep learning
social media