A Comparative Analysis of Machine Learning Techniques for Efficient Diabetes Prediction

##plugins.themes.academic_pro.article.sidebar##

Published Aug 9, 2024
https://doi.org/10.47164/ijngc.v15i2.1503
Download
Requires Subscription PDF
Statistic

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...
Volume 15, Issue 2, July 2024

##plugins.themes.academic_pro.article.main##

Tajinder Kaur
Sri Guru Teg Bahadur Khalsa College, Sri Anandpur Sahib, India
Sikander Singh Cheema
Punjabi University, Patiala, India
Lakhwinder Kaur
Punjabi University, Patiala

Abstract

In the healthcare sector, predictive analytics plays a vital role, presenting a challenging task but offering potential benefits in making informed decisions about patient health and treatment based on big data. This research paper delves into the realm of predictive analytics in healthcare, employing four distinct machine learning algorithms. The experiment involves the utilization of a dataset comprising patients’ medical records, upon which the four algorithms are applied. A comprehensive analysis is conducted using a diverse range of algorithms, including logistic regression, decision trees, random forests and support vector machines. These algorithms’ effectiveness is assessed using important measures like precision, recall, precision, accuracy and F1-score. By comparing the different machine learning techniques employed in the present study, the analysis aims to determine the most suitable algorithm for predicting diabetes.

##plugins.themes.academic_pro.article.details##

Creative Commons License

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

Author Biographies

Tajinder Kaur, Sri Guru Teg Bahadur Khalsa College, Sri Anandpur Sahib, India

 

Assistant Professor

PG department of Computer Science

Sikander Singh Cheema, Punjabi University, Patiala, India

Assistant Professor

Department of Computer Science and Engineering

Punjabi University Patiala

Lakhwinder Kaur, Punjabi University, Patiala

Professor

Department of Computer Science and Engineering,
Punjabi University, Patiala, India.

How to Cite
Tajinder Kaur, Cheema, S. S. ., & Lakhwinder Kaur. (2024). A Comparative Analysis of Machine Learning Techniques for Efficient Diabetes Prediction. International Journal of Next-Generation Computing, 15(2). https://doi.org/10.47164/ijngc.v15i2.1503
Crossref
Scopus
Google Scholar
Europe PMC

References

  1. A., S. 2023. Exploring the efficacy of machine learning algorithms for diabetes prediction: A comparative prediction. International Journal of Forensic Science and Technology Engineering.
  2. American Diabetes Association. 2018. Economic costs of diabetes in the u.s. in 2017. Diabetes Care 41, 5, 917–928. DOI: https://doi.org/10.2337/dci18-0007
  3. Bommer, C. et al. 2020. The global economic burden of diabetes in adults aged 20-79 years: A cost-of-illness study. The Lancet Diabetes & Endocrinology 5, 6, 423–430. DOI: https://doi.org/10.1016/S2213-8587(17)30097-9
  4. Chauhan, A. et al. 2023. Prediction of diabetes mellitus progression using supervised machine learning. Sensors. DOI: https://doi.org/10.3390/s23104658
  5. Edeh, M. et al. 2022. A classification algorithm-based hybrid diabetes prediction model. Frontiers in Public Health. DOI: https://doi.org/10.3389/fpubh.2022.829519
  6. Fu, X.-M. et al. 2023. Implementation of five machine learning methods to predict the 52-week blood glucose level in patients with type 2 diabetes. Frontiers in Endocrinology. DOI: https://doi.org/10.3389/fendo.2022.1061507
  7. G ̈undo ̆gdu, S. 2023. Efficient prediction of early-stage diabetes using xgboost classifier with random forest feature selection technique. Multimedia Tools and Applications. DOI: https://doi.org/10.1007/s11042-023-15165-8
  8. Hassan, C. A. U., Khan, M. S., and Shah, M. A. 2018. Comparison of machine learning algorithms in data classification. In Proceedings of the 2018 24th International Conference on Automation and Computing (ICAC). 1–6.
  9. International Diabetes Federation. 2021. Facts & figures. https://idf.org/about-diabetes/diabetes-facts-figures/. Retrieved from the International Diabetes Fed-eration website.
  10. Israt, J. et al. 2023. Data-driven diabetes risk factor prediction using machine learning algorithms with feature selection technique. Sustainability.
  11. Jr, D. W. H., Lemeshow, S., and Sturdivant, R. X. 2013. Applied Logistic Regression. John Wiley & Sons. DOI: https://doi.org/10.1002/9781118445112.stat06902
  12. Kangra, K. and Singh, J. 2023. Comparative analysis of predictive machine learning algorithms for diabetes mellitus. Bulletin of Electrical Engineering and Informatics 12, 1728–1737. DOI: https://doi.org/10.11591/eei.v12i3.4412
  13. Kibria, H. B. et al. 2022. An ensemble approach for the prediction of diabetes mellitus using a soft voting classifier with an explainable ai. Sensors. DOI: https://doi.org/10.3390/s22197268
  14. Kodama, S. et al. 2022. Predictive ability of current machine learning algorithms for type 2 diabetes mellitus: A meta-analysis. Journal of Diabetes Investigation 13, 900–908. DOI: https://doi.org/10.1111/jdi.13736
  15. Mao, Y. et al. 2022. Value of machine learning algorithms for predicting diabetes risk: A subset analysis from a real-world retrospective cohort study. Journal of Diabetes Investigation. DOI: https://doi.org/10.1111/jdi.13937
  16. Naseem, A. et al. 2022. Novel internet of things based approach toward diabetes prediction using deep learning models. Frontiers in Public Health. DOI: https://doi.org/10.3389/fpubh.2022.914106
  17. Noble, W. S. 2006. What is a support vector machine? Nature Biotechnology 24, 12, 1565–1567. DOI: https://doi.org/10.1038/nbt1206-1565
  18. Oumer, A. et al. 2023. Application of supervised machine learning algorithms for classification and prediction of type-2 diabetes disease status in afar regional state, northeastern ethiopia. Scientific Reports.
  19. Palimkar, P., Shaw, R. N., and Ghosh, A. 2022. Machine learning technique to prognosis diabetes disease: Random forest classifier approach. In Advanced Computing and Intelligent Technologies: Proceedings of ICACIT 2021. Springer, 219–244. DOI: https://doi.org/10.1007/978-981-16-2164-2_19
  20. Pasquier, J. Y., Sagahyroon, M., and Aloul, F. 2021. A machine learning approach to predicting diabetes complications. Healthcare 9, 12, 1712. DOI: https://doi.org/10.3390/healthcare9121712
  21. Rigatti, S. J. 2017. Random forest. Journal of Insurance Medicine 47, 1, 31–39. DOI: https://doi.org/10.17849/insm-47-01-31-39.1
  22. Rokach, L. and Maimon, O. 2005. Decision trees. In Data Mining and Knowledge Discovery Handbook. 165–192. DOI: https://doi.org/10.1007/0-387-25465-X_9
  23. Seto, H. et al. 2022. Gradient boosting decision tree becomes more reliable than logistic regression in predicting probability for diabetes with big data. Scientific Reports. DOI: https://doi.org/10.1038/s41598-022-20149-z
  24. Singh, R. et al. 2023. Prediction of diabetes using machine learning. International Journal of Research in Applied Science and Engineering Technology 11, 5232–5237. DOI: https://doi.org/10.22214/ijraset.2023.51696
  25. Sisodia, D. and Sisodia, D. S. 2018. Prediction of diabetes using classification algorithms. In Procedia Computer Science. Vol. 132. 1578–1585. DOI: https://doi.org/10.1016/j.procs.2018.05.122
  26. Uddin, S., Khan, A., Hossain, M. E., and Moni, M. A. 2019. Comparing different supervised machine learning algorithms for disease prediction. BMC Medical Informatics and Decision Making 19, 1, 281. DOI: https://doi.org/10.1186/s12911-019-1004-8
  27. Vijay, Y. and Nilam, N. 2023. Comparison of machine learning techniques for precision in measurement of glucose level in artificial pancreas. Mathematical Methods in the Applied Sciences.
  28. Weerts, H. J., Mueller, A. C., and Vanschoren, J. 2020. Importance of tuning hyperparameters of machine learning algorithms. arXiv preprint arXiv:2007.00209.