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Global Journal of Engineering and Technology Advances
International Peer reviewed Engineering Journal || Crossref DOI || Impact Factor 8.6 || ISSN: 2582-5003

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Research & review articles are invited for publication in September 2026 (Vol. 28, Issue 3) || Submission: up to 28th September || Editorial decision: within 48 hrs.

Early diagnosing diabetes using data mining algorithms

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  • Early diagnosing diabetes using data mining algorithms

Rasha Rokan Ismail *

Department of Computer Science, Diyala University President, Diyala, Iraq.
 
Research Article
Global Journal of Engineering and Technology Advances, 2023, 16(02), 106–113.
Article DOI: 10.30574/gjeta.2023.16.2.0141
DOI url: https://doi.org/10.30574/gjeta.2023.16.2.0141
Received on 11 June 2023; revised on 17 July 2023; accepted on 20 July 2023
 
Diabetes has become a widespread and long lasting condition that continues to impact an increasing number of individuals across the globe. It is crucial to highlight the significance of accurately identifying, predicting, managing and treating diabetes in order to address this growing concern. Utilizing sophisticated data analysis techniques to examine data relating to diabetes can significantly enhance the early detection and prediction of this ailment, along with its associated complications like low or high blood sugar levels. The findings clearly demonstrate that the decision tree algorithm proves to be the most effective approach in promptly diagnosing diabetes patients and ensuring they receive timely access to suitable treatment options.
 
Diabetes; SVM; TREE; Regression
 
https://gjeta.com/sites/default/files/fulltext_pdf/GJETA-2023-0141.pdf

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Rasha Rokan Ismail. Early diagnosing diabetes using data mining algorithms. Global Journal of Engineering and Technology Advances, 2023, 16(2), 106-113. Article DOI: https://doi.org/10.30574/gjeta.2023.16.2.0141

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