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

Formative Evaluation of Digital Health Interventions (2491) Artificial Intelligence (1421) Machine Learning (1501) Mobile Health (mhealth) (2982) Artificial Intelligence in Diabetes Care and Prevention (29) Apps, Mobile, Wearables for Diabetes (306) Diabetes Prevention (86)

Published on in Vol 7 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/50328, first published .
A Mobile App That Addresses Interpretability Challenges in Machine Learning–Based Diabetes Predictions: Survey-Based User Study

A Mobile App That Addresses Interpretability Challenges in Machine Learning–Based Diabetes Predictions: Survey-Based User Study

A Mobile App That Addresses Interpretability Challenges in Machine Learning–Based Diabetes Predictions: Survey-Based User Study

Authors of this article:

Rasha Hendawi1 Author Orcid Image ;   Juan Li1 Author Orcid Image ;   Souradip Roy1 Author Orcid Image
Article Authors Cited by (2) Tweetations (2) Metrics

Rasha Hendawi   1 , PhD ;   Juan Li   1 , PhD ;   Souradip Roy   1 , PhD

1 North Dakota State University, Fargo, ND, United States

Corresponding Author: