Early diagnosing diabetes using data mining algorithms
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
Publication history:
Received on 11 June 2023; revised on 17 July 2023; accepted on 20 July 2023
Abstract:
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.
Keywords:
Diabetes; SVM; TREE; Regression
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Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0