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Deep learning for predicting drug-related problems in diabetes patients

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 15 No. 3 (2025)

DOI
10.11591/ijece.v15i3.pp2998-3009
ISSN
2722-2578, 2088-8708
Published
2025-06-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Machine learning and deep learning have made advances in the healthcare domain. In this study, we aim to apply deep learning models to predict the drug-related problems (DRPs) status for diabetes patients. Also, to determine the appropriate model to use for classification using deep learning algorithms or machine learning methods to investigate which one performed better results for tabular data by comparing the achieved deep learning results with the machine learning methods to figure out which one gives better results. To apply the deep learning models, the same criteria that were applied in the previous study have been implemented in this investigation, and the same dataset was used. The results show that the machine learning algorithms especially the random forests for predicting the DRPs status outperform the deep learning models. For classification tasks in healthcare for tabular data, the findings of this study show that machine learning methods are the appropriate model instead of using deep learning to apply classification.

Collaboration network of the authors

5 authors · 7 co-authorship links · 3 across different organisations

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  • Hashemite University
  • Yarmouk University
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Yasser QawasmehHashemite University1
Addy AlQuraanHashemite University1
Ahmed Fawzi OtoomHashemite University1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
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