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DOI Prefix: 10.47001/IRJIET

Diabetes Disease Prediction Powered by Fused Machine Learning

Dr. Kavyashree NAssistant Professor, Department of MCA, Sri Siddhartha Institute of Technology, Tumkur, Karnataka, IndiaRuba Naaz SStudent, Department of MCA, Sri Siddhartha Institute of Technology, Tumkur, Karnataka, IndiaKruthika V RStudent, Department of MCA, Sri Siddhartha Institute of Technology, Tumkur, Karnataka, India

Vol 7 No 6 (2023): Volume 7, Issue 6, June 2023 | Pages: 220-223

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 12-07-2023

doi Logo doi.org/10.47001/IRJIET/2023.706033

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Abstract

To avoid diseases, early disease prediction is crucial in the medical industry. One of the most hazardous diseases in the world is diabetes. Our dietary habits in modern lifestyles are often high in sugar and fat, which has raised the risk of diabetes. Understanding the disease's symptoms is crucial for making predictions about it. Machine-learning (ML) techniques are useful right now for identifying diseases. To anticipate and analyse in this case, we employed the support vector machine and the k-nearest neighbour technique. These models examine the data set to evaluate whether a positive or negative diabetes diagnosis has been made.

Keywords

Machine-learning, k-nearest neighbour technique, support vector


Citation of this Article

Dr. Kavyashree N, Ruba Naaz S, Kruthika V R, “Diabetes Disease Prediction Powered by Fused Machine Learning” Published in International Research Journal of Innovations in Engineering and Technology - IRJIET, Volume 7, Issue 6, pp 220-223, June 2023. Article DOI https://doi.org/10.47001/IRJIET/2023.706033

Licence Copyright (c) 2026 International Research Journal of Innovations in Engineering and Technology creative common licence This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
References
  1. Computer Vision and Machine Intelligence in Medical Image Analysis by F. Islam, R. Ferdousi, S. Rahman, and H. Y. Bushra. 2019; London, UK: Springer.
  2. The World Health Organisation (WHO). Leading Causes of Death and Disability Worldwide: 2000-2019, according to WHO. 22 October 2021, accessed. [Online].
  3. Asuncion and Frank, A. (2010). Machine Learning Repository at UC Irvine.
  4. G. Pradhan, R. Pradhan, and B. Khandelwal, "A Study on Various Machine Learning Algorithms Used for Prediction of Diabetes Mellitus," in Advances in Intelligent Systems and Computing, vol. 1248, Soft Computing Techniques and Applications. London.
  5. S. Kumari, D. Kumar, and M. Mittal, "An ensemble approach for classification and prediction of diabetes mellitus using soft voting classifier," International Journal of Cognitive Engineering, Volume 2, June 2021, Pages 40–46.
  6. M. A. Sarwar, N. Kamal, W. Hamid, and M. A. Shah., "Prediction of diabetes using machine learning algorithms in healthcare," in Proc. 24th Int. Conf. Autom. Comput. (ICAC), Sep. 2018, pp. 6-7.
  7. S. K. Dey, A. Hossain, and M. M. Rahman., Implementation of a web application to forecast diabetic disease: A method employing machine learning algorithms, in Proceedings of the 21st International Conference on Computer and Information Technology (ICCIT).
  8. "Diabetes disease prediction using machine learning on big data of healthcare," by A. Mir and S. N. Dhage, 4th International Conference on Computer Communication and Control Automation (ICCUBEA), August 2018.
  9. S. Subashree and S. Saru. Diabetes Analysis and Prediction Using Machine Learning. 22 October 2022, accessed. [Online].
  10. P. Sonar and K. JayaMalini, "Diabetes prediction using different machine learning approaches," in Proc. 3rd Int. Conf. Comput. Methodologies Commun. (ICCMC), Mar. 2019, pp. 367-371.

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