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Proceedings of the 2023 Eighth International Conference on Research in Intelligent Computing in Engineering

Annals of Computer Science and Information Systems, Volume 38

Applications of Machine Learning for Diabetes Prediction

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DOI: http://dx.doi.org/10.15439/2023R43

Citation: Proceedings of the 2023 Eighth International Conference on Research in Intelligent Computing in Engineering, Pradeep Kumar, Manuel Cardona, Vijender Kumar Solanki, Tran Duc Tan, Abdul Wahid (eds). ACSIS, Vol. 38, pages 1–6 ()

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Abstract. The use of machine learning techniques has drawn more attention due to its potential to improve early identification and intervention in diabetes, a critical global health concern. This article offers an extensive overview of the various machine learning algorithms used in diabetes prediction, including ensemble techniques, logistic regression, support vector machines, decision trees, and neural networks. The research closely examines how these algorithms make use of a variety of data sources, including wearable sensor data, electronic health records, clinical data, and genetic information. The report also emphasizes the difficulties that these applications face, including as interpretability, model integration into clinical procedures, and ethical issues. This review elucidates the significant influence of machine learning on diabetes prediction, paving the way for more useful risk assessment, individualized therapies, and improved patient outcomes. It does this by thoroughly examining recent studies and their conclusions.

References

  1. International Diabetes Federation https://idf.org fetched 20.09.2023
  2. Pitt, Bertram, et al. "Cardiovascular events with finerenone in kidney disease and type 2 diabetes." New England journal of medicine 385.24 (2021): 2252-2263.
  3. Jyotismita Chaki, S. Thillai Ganesh, S.K Cidham, S. Ananda Theertan, Machine learning and artificial intelligence based Diabetes Mellitus detection and self-management: A systematic review, Journal of King Saud University - Computer and Information Sciences, Volume 34, Issue 6, Part B, 2022, Pages 3204-3225, ISSN 1319-1578, https://doi.org/10.1016/j.jksuci.2020.06.013.
  4. Naz H, Ahuja S. Deep learning approach for diabetes prediction using PIMA Indian dataset. J Diabetes Metab Disord. 2020 Apr 14;19(1):391-403. http://dx.doi.org/10.1007/s40200-020-00520-5.
  5. LaValley, Michael P. "Logistic regression." Circulation 117.18 (2008): 2395-2399
  6. Al Jarullah, Asma A. "Decision tree discovery for the diagnosis of type II diabetes." 2011 International conference on innovations in information technology. IEEE, 2011.
  7. N. Mohan and V. Jain, "Performance Analysis of Support Vector Machine in Diabetes Prediction," 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India, 2020, pp. 1-3, http://dx.doi.org/10.1109/ICECA49313.2020.9297411.
  8. El_Jerjawi, Nesreen Samer, and Samy S. Abu-Naser. "Diabetes prediction using artificial neural network." (2018).
  9. Aslan MF, Sabanci K. A Novel Proposal for Deep Learning-Based Diabetes Prediction: Converting Clinical Data to Image Data. Diagnostics (Basel). 2023 Feb 20;13(4):796. http://dx.doi.org/10.3390/diagnostics13040796. PMID: 36832284; PMCID: PMC9955314.
  10. Priyanka Rajendra, Shahram Latifi, Prediction of diabetes using logistic regression and ensemble techniques, Computer Methods and Programs in Biomedicine Update, Volume 1, 2021, 100032, ISSN 2666-9900,https://doi.org/10.1016/j.cmpbup.2021.100032.
  11. W. Chen, S. Chen, H. Zhang and T. Wu, "A hybrid prediction model for type 2 diabetes using K-means and decision tree," 2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS), Beijing, China, 2017, pp. 386-390, http://dx.doi.org/10.1109/ICSESS.2017.8342938.
  12. K. VijiyaKumar, B. Lavanya, I. Nirmala and S. S. Caroline, "Random Forest Algorithm for the Prediction of Diabetes," 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN), Pondicherry, India, 2019, pp. 1-5, http://dx.doi.org/10.1109/ICSCAN.2019.8878802.
  13. Patil, Ratna, et al. "A modified mayfly-SVM approach for early detection of type 2 diabetes mellitus." Int. J. Electr. Comput. Eng 12.1 (2022): 524.
  14. M. T. Islam, H. R. H. Al-Absi, E. A. Ruagh and T. Alam, "DiaNet: A Deep Learning Based Architecture to Diagnose Diabetes Using Retinal Images Only," in IEEE Access, vol. 9, pp. 15686-15695, 2021, http://dx.doi.org/10.1109/ACCESS.2021.3052477.
  15. Farajollahi, Boshra, et al. "Diabetes diagnosis using machine learning." Frontiers in Health Informatics 10.1 (2021): 65.
  16. Rufo, Derara Duba, et al. "Diagnosis of diabetes mellitus using gradient boosting machine (LightGBM)." Diagnostics 11.9 (2021): 1714.
  17. Ashima Singh, Arwinder Dhillon, Neeraj Kumar, M. Shamim Hossain, Ghulam Muhammad, and Manoj Kumar. 2021. EDiaPredict: An Ensemble-based Framework for Diabetes Prediction. ACM Trans. Multimedia Comput. Commun. Appl. 17, 2s, Article 66 (June 2021), 26 pages. https://doi.org/10.1145/3415155
  18. Taha, Altyeb Altaher, and Sharaf Jameel Malebary. "A hybrid meta-classifier of fuzzy clustering and logistic regression for diabetes prediction." Computers, Materials and Continua 71.2 (2022): 6089-105.
  19. A. Yahyaoui, A. Jamil, J. Rasheed and M. Yesiltepe, "A Decision Support System for Diabetes Prediction Using Machine Learning and Deep Learning Techniques," 2019 1st International Informatics and Software Engineering Conference (UBMYK), Ankara, Turkey, 2019, pp. 1-4, http://dx.doi.org/10.1109/UBMYK48245.2019.8965556.
  20. Arora, Nitin, et al. "A Novel Architecture for Diabetes Patients’ Prediction Using K-Means Clustering and SVM." Mathematical Problems in Engineering 2022 (2022).
  21. Jader, Rasool, and Sadegh Aminifar. "Fast and Accurate Artificial Neural Network Model for Diabetes Recogni-tion." NeuroQuantology 20.10 (2022): 2187-2196.
  22. Yadav, D.C., Pal, S. (2021). An Ensemble Approach for Classification and Prediction of Diabetes Mellitus Disease. In: Mathur, R., Gupta, C.P., Katewa, V., Jat, D.S., Yadav, N. (eds) Emerging Trends in Data Driven Computing and Communications. Studies in Autonomic, Data-driven and Industrial Computing. Springer, Singapore. https://doi.org/10.1007/978-981- 16-3915-9_18
  23. Kopitar, L., Kocbek, P., Cilar, L. et al. Early detection of type 2 diabetes mellitus using machine learning-based prediction models. Sci Rep 10, 11981 (2020). https://doi.org/10.1038/s41598-020-68771-z
  24. Tuppad, Ashwini, and Shantala Devi Patil. "Machine learning for diabetes clinical decision support: a review." Advances in Computational Intelligence 2.2 (2022): 22.
  25. Berchialla, Paola, et al. "Prediction of treatment outcome in clinical trials under a personalized medicine perspective." Scientific Reports 12.1 (2022): 4115.
  26. Sun, Y. L. & Zhang, D. L., 2019. Machine Learning Techniques for Screening and Diagnosis of Diabetes: A Survey. Technical Gazette, Volume 26, p. 872– 880
  27. Qawqzeh, Y. K. et al., 2020. Classification of diabetes using photoplethysmogram (PPG) waveform analysis: logistic regression modeling. BioMed Research International, pp. 1-20.
  28. Sisodia, D. & Sisodia, D. S., 2018. Prediction of diabetes using classification algorithms. Procedia Computer Science, Volume 132, p. 1578–1585.
  29. Hussain, A. & Naaz, S., 2021. Prediction of diabetes mellitus: comparative study of various machine learning models. Advances in Intelligent Systems and Computing, Volume 1166, p. 103–115.
  30. Choubey, D. K. et al., 2020. Comparative analysis of classification methods with PCA and LDA for diabetes. Current Diabetes Reviews, 16(8), p. 833–850.
  31. Gujral, S., 2017. Early diabetes detection using machine learning: a review. International Journal for Innovative Research in Science & Technology, 3(10), pp. 45-60.
  32. Tigga, N. P. & Garg, S., 2020. Prediction of type 2 diabetes using machine learning classification methods. Procedia Computer Science, Volume 167, p. 706–716.
  33. Mercaldo, F., Nardone, V. & Santone, A., 2017. Diabetes mellitus affected patients classification and diagnosis through machine learning techniques. Procedia Computer Science, Volume 112, p. 2519–2528.
  34. Dutta A, Hasan MK, Ahmad M, Awal MA, Islam MA, Masud M, Meshref H. Early Prediction of Diabetes Using an Ensemble of Machine Learning Models. International Journal of Environmental Research and Public Health. 2022; 19(19):12378. https://doi.org/10.3390/ijerph191912378
  35. U. Ahmed et al., "Prediction of Diabetes Empowered With Fused Machine Learning," in IEEE Access, vol. 10, pp. 8529-8538, 2022, http://dx.doi.org/10.1109/ACCESS.2022.3142097.
  36. Whig, P., Gupta, K., Jiwani, N. et al. A novel method for diabetes classification and prediction with Pycaret. Microsyst Technol 29, 1479–1487 (2023). https://doi.org/10.1007/s00542-023-054732