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The Applicability of Some Machine Learning Algorithms in the Prediction of Type 2 Diabetes Cover

The Applicability of Some Machine Learning Algorithms in the Prediction of Type 2 Diabetes

Open Access
|Jul 2024

References

  1. Al-Gharabawi, F.W. & Abu-Naser, S.S. (2023). Machine Learning-Based Diabetes Prediction: Feature Analysis and Model Assessment. International Journal of Academic Engineering Research (IJAER) 7 (9), 10-17.
  2. Alehegn, M., Joshi, R.R. & Mulay, P. (2019). Diabetes analysis and prediction using random forest, KNN, Naïve Bayes, and J48: an ensemble approach. Int J Sci Technol Res., 8(9), 1346–1354.
  3. Amour Diwani, S. & Sam, A. (2014). Diabetes forecasting using supervised learning techniques. Adv. Comput. Sci.: Int. J., 3(5), 10–18, Retrieved from: http://www.acsij.org/acsij/article/view/156.
  4. Anuja Kumari, V. & Chitra, R. (2013). Classification of diabetes disease using support vector machine”, Int. J. Eng. Res. Appl., 3, 1797–1801.
  5. Beghriche, T., Djerioui, M., Brik, Y., Attallah, B. & Belhaouari, S.B. (2021) An Efficient Prediction System for Diabetes Disease Based on Deep Neural Network. Hindawi Complexity. Retrieved at: https://doi.org/10.1155/2021/6053824">https://doi.org/10.1155/2021/6053824.
  6. Daghistani, T. & Alshammari, R. (2020). Comparison of statistical logistic regression and random forest machine learning techniques in predicting diabetes. J. Adv. Inf.Technol., 11(2), 78-83.
  7. DeFronzo, R.A., Ferrannini, E., Groop, L, Henry, R.R., Herman, W.H., Holst, J.J., Hu, F.B., Kahn, C.R., Raz, I., Shulman, G.I., Simonson, D.C., Testa, M.A. & Weiss, R. (2015). Type 2 diabetes mellitus. Nat Rev Dis Primers, 1,15019. doi:10.1038/nrdp.2015.19.
  8. Dewangan, A.K. & Agrawal, P. (2015). Classification of diabetes mellitus using machine learning techniques. Int. J. Eng. Appl. Sci., 2(5), 145-148.
  9. Islam, M.M.F., Ferdousi, R., Rahman, S. & Bushra, H.Y. (2020). Likelihood prediction of diabetes at early stage using data mining techniques, in Gupta M, Konar D, Bhattacharyya S, Biswas S (eds) Computer vision and machine intelligence in medical image analysis. Advances in intelligent systems and computing”, 992, Springer, Singapore, 113–125. Retrieved from: 10.1007/978-981-13-8798-2_12.
  10. Iyer, A., Jeyalatha, S. & Sumbaly, R. (2015). Diagnosis of Diabetes Using Classification Mining Techniques. International Journal of Data Mining & Knowledge Management Process (IJDKP), 5, 1-14. Retrieved from: https://doi.org/10.5121/ijdkp.2015.5101">https://doi.org/10.5121/ijdkp.2015.5101.
  11. Madhu, B., Aerranagula, V., Mahomad, R., Ravindernaik, V., Madhavi, K. & Krishna, G. (2023) Techniques of Machine Learning for the Purpose of Predicting Diabetes Risk in PIMA Indians. E3S Web of Conferences, 011. Retrieved at: https://doi.org/10.1051/e3sconf/202343001151">https://doi.org/10.1051/e3sconf/202343001151.
  12. Malik, S., Harous, S. & El-Sayed, H. (2021). Comparative Analysis of Machine Learning Algorithms for Early Prediction of Diabetes Mellitus in Women. Modelling and Implementation of Complex Systems”, Springer International Publishing. Retrieved from: https://www.springerprofessional.de/en/comparative-analysis-of-machine-learning-algorithms-for-early-pr/18351326.
  13. Mujumdar, A. & Vaidehi, V. (2019). Diabetes Prediction using Machine Learning Algorithms. Procedia Computer Science, 165, 292–299.
  14. Olivera, A.R., Roesler, V., Iochpe, C., Schmidt, M.I., Vigo. Á., Barreto S.M., Duncan, B.B. (2017). Sao Paulo Med J.,135 (3), 234-46.
  15. Perveen, S., Shahbaz, M., Guergachi, A. & Keshavjee, K. (2016). Performance analysis of data mining classification techniques to predict diabetes. Procedia Comput. Sci., 82, 115–121. Retrieved from https://dspace.library.uvic.ca/bitstream/handle/1828/9390/Keshavjee_Karim_ProcediaComputSci_2016.pdf?sequence=1&isAllowed=y
  16. Purnami, S.W., Embong, A., Zainand, J.M. & Rahayu, S.P. (2019). A New Smooth Support Vector Machine and Its Applications in Diabetes Disease Diagnosis/ Journal of Computer Science. 5(12), 1003-1008.
  17. Rhee, S.Y., Sung, J.M., Kim, S., Cho, I.J., Lee, S.E. & Chang, H.J. (2019). Development and Validation of a Deep Learning Based Diabetes Prediction System Using a Nationwide Population-Based Cohort. Diabetes Metab J., 45, 515-525. Retrieved at: https://doi.org/10.4093/dmj.2020.0081">https://doi.org/10.4093/dmj.2020.0081.
  18. Sanakal, R. & Jayakumari, S.T. (2014). Prognosis of diabetes using data mining approach-fuzzy C means clustering and support vector machine. Int. J. Comput. TrendsTechnol., 11, 94–98.
  19. Sen, S.K. & Dash, S. (2014). Application of Meta Learning Algorithms for the Prediction of Diabetes Disease. International Journal of Advance Research in Computer Science and Management Studies, 2, 396-401.
  20. Sisodia, D. & Sisodia, D.S. (2018). Prediction of diabetes using classification algorithms. Procedia Comput. Sci. 132, 1578–1585.
  21. Soliman, O.S. & AboElhamd, E. (2014). Classification of Diabetes Mellitus using Modified Particle Swarm Optimization and Least Squares Support Vector Machine. Retrieved from arXiv:1405.0549.
  22. Sridar, K. & Shanthi, D. (2014). Medical diagnosis system for the diabetes mellitus by using back propagation-Apriori algorithms. J. Theor. Appl. Inf. Technol., 68(1), 36-43.
  23. Tasin, I., Ullah, T., Sanjida, N. & Khan, I.R. (2023). Diabetes prediction using machine learning and explainable AI techniques. Healthc. Technol. Lett., 10, 1–10. DOI: 10.1049/htl2.12039.
  24. Tigga, N.P. & Garg, S. (2019). Predicting type 2 Diabetes using Logistic Regression. Proceedings of the Fourth International Conference on Microelectronics, Computing and Communication Systems MCCS, Lecture Notes of Electrical Engineering, Springer.
  25. Yu, W., Liu, T., Valdez, R., Gwinn, M. & Khoury, M.J. (2010). Application of support vector machine modeling for prediction of common diseases: the case of diabetes and pre-diabetes. BMC Med. Inform. Decis. Mak. 10(16). Retrieved from doi:10.1186/1472-6947-10-16.
  26. Zou, Q., Qu, K., Luo, Y., Yin, D., Ju, Y. & Tang, H. (2018). Predicting Diabetes Mellitus with Machine Learning Techniques. Frontiers in genetics, 9, 515. Retrieved from https://www.frontiersin.org/articles/10.3389/fgene.2018.00515/full.
Language: English
Page range: 246 - 257
Published on: Jul 3, 2024
Published by: Bucharest University of Economic Studies
In partnership with: Paradigm Publishing Services
Publication frequency: 1 times per year

© 2024 Oana Vîrgolici, Laura Gabriela Tănăsescu, published by Bucharest University of Economic Studies
This work is licensed under the Creative Commons Attribution 4.0 License.