Predicting the diabetes risk by analyzing symptoms using data mining techniques

RF is suggested to be the best-intended model to predict diabetes risk along with 10-fold cross-validation. It must be a part of the decision-making along with the provided implications and contributions.

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Publication Details

Journal
Informatics for Health and Social Care
Published
2025-12-31
DOI
https://doi.org/10.1080/17538157.2025.2602517
Primary Topic
Artificial Intelligence in Healthcare
Type
article
Field-Weighted Citation Impact
0.00
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article

Predicting the diabetes risk by analyzing symptoms using data mining techniques

Rahaf Alhamouri, Dania Rahhal, Ahmad Alaiad
Informatics for Health and Social Care
Artificial Intelligence in Healthcare
article

Predicting the diabetes risk by analyzing symptoms using data mining techniques

Rahaf Alhamouri, Dania Rahhal, Ahmad Alaiad
article en

Abstract

RF is suggested to be the best-intended model to predict diabetes risk along with 10-fold cross-validation. It must be a part of the decision-making along with the provided implications and contributions.

Informatics for Health and Social Care
Jordan University of Science and Technology (JO)
Peace, Justice and strong institutions
Openalex Percentile: Top 26%
Artificial Intelligence in Healthcare
DOISourceOpenAlex
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Predicting the diabetes risk by analyzing symptoms using data mining techniques — Rahaf Alhamouri, Dania Rahhal, et al. · Informatics for Health and Social Care (2025) | TGRS Research Map | TGRS