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

Development and Evaluation of Research Methods, Instruments and Tools (848) Clinical Information and Decision Making (2137) mHealth in a Clinical Setting (799) Formative Evaluation of Non-Ehealth Innovations (228)

Published on in Vol 10 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/81039, first published .
Complication Risk Classification in Children and Adolescents With Type 1 Diabetes: Interpretable Machine Learning Study Based on Saudi Clinical Guidelines

Complication Risk Classification in Children and Adolescents With Type 1 Diabetes: Interpretable Machine Learning Study Based on Saudi Clinical Guidelines

Complication Risk Classification in Children and Adolescents With Type 1 Diabetes: Interpretable Machine Learning Study Based on Saudi Clinical Guidelines

Authors of this article:

Jalilah Fllatah1 Author Orcid Image ;   Haneen Banjar1, 2, 3, 4 Author Orcid Image
Article Authors Cited by Tweetations Metrics

Jalilah Fllatah   1 , BCS ;   Haneen Banjar   1, 2, 3, 4 , PhD

1 Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia

2 Center of Research Excellence in Artificial Intelligence and Data Science, King Abdulaziz University, Jeddah, Saudi Arabia

3 Institute of Genomic Medicine Sciences, King Abdulaziz University, Jeddah, Saudi Arabia

4 Centre of Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah, Saudi Arabia

Corresponding Author:

  • Jalilah Fllatah, BCS
  • Department of Computer Science
  • Faculty of Computing and Information Technology, King Abdulaziz University
  • P.O. Box 80200
  • Jeddah 21589
  • Saudi Arabia
  • Phone: 966 544027109
  • Email: jalilahfallatah@hotmail.com