Predicting Type 2 Diabetes Mellitus using Machine Learning Algorithms

Authors

  • Nisreen Sulayman Damascus University

Keywords:

Type 2 Diabetes Mellitus, Machine Learning, XGBoost Model, Logistic Regression

Abstract

 

Purpose: to build an effective prediction model based on machine learning (ML) algorithms for the risk of type 2 (non-insulin-dependent) Diabetes Mellitus (T2DM).

Methods: I developed two machine learning prediction models based on extreme gradient boosting (XGBoost) and logistic regression (LR). To evaluate the ML prediction models I used the Pima Indian Diabetes dataset (PIDD). The dataset is from the National Institute of Diabetes and Digestive and Kidney Diseases and consists of 500 non-diabetic patients and 268 diabetes patients.

Results: Models' performance was evaluated using six performance criteria. XGBoost model outperforms the logistic regression. The XGBoost model achieved: area under receiver operating characteristic curve (AUROC) = 85%, sensitivity = 71%, specificity = 81%, accuracy =77%, precision = 67%, and F1-score=69% respectively.

Conclusion: This study showed that the XGBoost ML algorithm can be applied to predict individuals at high risk of T2DM in the early phase, which has a strong potential to control diabetes mellitus.

Downloads

Published

2022-11-17

How to Cite

1.
Sulayman N. Predicting Type 2 Diabetes Mellitus using Machine Learning Algorithms. Tuj-eng [Internet]. 2022Nov.17 [cited 2024Apr.26];44(5):89-100. Available from: https://journal.tishreen.edu.sy/index.php/engscnc/article/view/13476

Issue

Vol. 44 No. 5 (2022): Tishreen University Journal of Research and Scientific Studies - Engineering Sciences Series

Section

Articles

License

Copyright (c) 2022 ttps://creativecommons.org/licenses/by-nc-sa/4.0/

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

The authors retain the copyright and grant the right to publish in the magazine for the first time with the transfer of the commercial right to Tishreen University Journal for Research and Scientific Studies - Engineering Sciences Series

 Under a CC BY- NC-SA 04 license that allows others to share the work with of the work's authorship and initial publication in this journal. Authors can use a copy of their articles in their scientific activity, and on their scientific websites, provided that the place of publication is indicted in  Tishreen University Journal for Research and Scientific Studies - Engineering Sciences Series   .  The Readers have the right to send, print and subscribe to the initial version of the article, and the title of  Tishreen University Journal for Research and Scientific Studies - Engineering Sciences Series     Publisher

journal uses a CC BY-NC-SA license which mean

You are free to:

  • Adapt — remix, transform, and build upon the material
  •  
  • The licensor cannot revoke these freedoms as long as you follow the license terms.

 

  • Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
  • NonCommercial — You may not use the material for commercial purposes.
  • ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
  • No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.