This paper is in the following e-collection/theme issue:

Machine Learning (52) Machine Learning (1400) Clinical Information and Decision Making (1339) Artificial Intelligence (1214) Anxiety and Stress Disorders (1013) Decision Support for Health Professionals (1145) AI Data (2)

Published on in Vol 2 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/41868, first published .
Prediction of Chronic Stress and Protective Factors in Adults: Development of an Interpretable Prediction Model Based on XGBoost and SHAP Using National Cross-sectional DEGS1 Data

Prediction of Chronic Stress and Protective Factors in Adults: Development of an Interpretable Prediction Model Based on XGBoost and SHAP Using National Cross-sectional DEGS1 Data

Prediction of Chronic Stress and Protective Factors in Adults: Development of an Interpretable Prediction Model Based on XGBoost and SHAP Using National Cross-sectional DEGS1 Data

Authors of this article:

Arezoo Bozorgmehr1 Author Orcid Image ;   Birgitta Weltermann1 Author Orcid Image
Article Authors Cited by (2) Tweetations (4) Metrics

Journals

  1. Abd Al-Alim M, Mubarak R, M. Salem N, Sadek I. A machine-learning approach for stress detection using wearable sensors in free-living environments. Computers in Biology and Medicine 2024;179:108918 View