Manuscript Number : IJSRST229394

A Systematic Review on Machine Learning Algorithms for Diagnosis of Diabetes in Health Care Systems

Authors(4) :-, Ch .Swetha, Dr. Mahesh Kotha, D Anusha

Diabetes is an ongoing metabolic problem that influences an expected 463 million individuals around the world. Meaning to work on the therapy of individuals with diabetes, computerized wellbeing has been generally taken on as of late and produced a gigantic measure of information that could be utilized for additional administration of this persistent sickness. Exploiting this moves toward that utilization computerized reasoning and explicitly profound learning, an arising sort of AI, have been broadly taken on with promising outcomes. In this paper, we present a complete survey of the utilizations of profound advancing inside the field of diabetes. We led a deliberate writing search and recognized three primary regions that utilization this methodology: finding of diabetes, glucose the executives, and determination of diabetes-related inconveniences. The hunt brought about the determination of 40 unique exploration articles, of which we have summed up the critical data about the utilized learning models, improvement process, principal results, and pattern strategies for execution assessment. Among the examined writing, it is to be noticed that different profound learning strategies and systems have accomplished state of-the-craftsmanship execution in numerous diabetes-related assignments by outflanking regular AI draws near. In the mean time, we distinguish a few restrictions in the ongoing writing, like an absence of information accessibility and model interpretability. The fast advancements in profound learning and the expansion in accessible information offer the likelihood to address these difficulties soon and permit the far and wide arrangement of this innovation in clinical settings.

Authors and Affiliations


Assistant Professor, G Narayanamma Institute of Technology and Science, Hyderabad, India
Ch .Swetha
Assistant Professor, AI Department, Vidya Jyothi Institute of Technology, Hyderabad, India
Dr. Mahesh Kotha
Assistant Professor, CSE- (AI&ML) Department, CMR Technical Campus, Hyderabad, India
D Anusha
Assistant Professor, AI Department, Vidya Jyothi Institute of Technology, Hyderabad, India

Diabetes, deep learning, machine learning, diabetic complications, artificial intelligence, prediction.

  1. Iyer A, Jeyalatha S, Sumbaly R (2015) Diagnosis of diabetes using classification mining techniques. Int J Data Min Knowl Manag Process 5(1): 1–14.
  2. Taiyu Zhu,"Deep Learning for Diabetes: A Systematic Review",2168-2194 (c) 2020 IEEE.
  3. Barik S, Mohanty S, Mohanty S, Singh D (2021) Analysis of prediction accuracy of diabetes using classifier and hybrid machine learning techniques. In: Mishra D, Buyya R, Mohapatra P, Patnaik S (eds) Intelligent and cloud computing. Smart innovation, systems and technologies, vol 153. Springer, Singapore, pp 399–409.
  4. Ravindra Changala, “Development of Predictive Model for Medical Domains to Predict Chronic Diseases (Diabetes) Using Machine Learning Algorithms and Classification Techniques”, ARPN Journal of Engineering and Applied Sciences, VOL. 14, NO. 6, MARCH 2019, ISSN 1819-6608
  5. Ephzibah EP (2011) A hybrid genetic-fuzzy expert system for effective heart disease diagnosis. In: Wyld DC, Wozniak M, Chaki N, Meghanathan N, Nagamalai D (eds) Advances in computing and information technology. first international conference, ACITY 2011, July 2011. Communications in computer and information science, vol 198. Springer, Berlin, Heidelberg, pp 115-121.
  6. M.Rajeswari,"A Review of Diabetic Prediction Using Machine Learning Techniques", International Journal of Engineering and Techniques - Volume 5 Issue 4,July 2019.
  7. Ravindra Changala, “Statistical Models in Data Mining: A Bayesian Classification” in International Journal of Recent Tredns in Engineering & Research (IJRTER), volume 3, issue 1, pp.290-293. in 2017.
  8. Zheng T, Xie W, Xu LL, He XY, Zhang Y, You MR et al (2017) A machine learning-based framework to identify type 2 diabetes through electronic health records. Int J Med Inform 97:120–127.
  9. Md. Kamrul Hasan, Md. Ashraful Alam, D. Das, E. Hussain, and M. Hasan, “Diabetes prediction using ensembling of different machine learning classifiers,” IEEE Acess, vol. 8, pp. 76516–76531, 2020.
  10. Umair Muneer,"Machine Learning Based Diabetes Classification and Prediction for Healthcare Applications", Hindawi,Journal of Healthcare Engineering Volume 2021, Article ID 9930985.
  11. V. Jackins, S. Vimal, M. Kaliappan, and M. Y. Lee, “AI-based smart prediction of clinical disease using random forest classifier and Naive Bayes,” Fe Journal of Supercomputing, vol. 77, no. 5, pp. 5198–5219, 2021.
  12. Ravindra Changala, “Evaluation and Analysis of Discovered Patterns Using Pattern Classification Methods In Text Mining” in  ARPN Journal of Engineering and Applied Sciences,Volume 13, Issue 11, Pages 3706-3717 with ISSN:1819-6608 in June 2018.
  13. Aishwarya M,"Diabetes Prediction using Machine Learning Algorithms", Procedia Computer Science 165 (2019) 292–299.
  14. N. Sneha and T. Gangil, “Analysis of Diabetes Mellitus for Early Prediction Using Optimal Feature Selection,” Journal of Big Data, vol. 6, 2019.
  15. Ravindra Changala, “Integrating Different Machine Learning Techniques for Assessment and Forecasting of Data” in Springer series, August-2015.
  16. 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.
  17. Khan NS, Muaz MH, Kabir A, Islam MN (2019) A machine learning-based intelligent system for predicting diabetes. Int J Big Data Anal Healthc 4(2):1.
  18. Nai-Arun N, Moungmai R (2015) Comparison of classifiers for the risk of diabetes prediction. Procedia Comput Sci 69:132–142.
  19. Roshi Saxena,"A Comprehensive Review of Various Diabetic Prediction Models: A Literature Survey", Hindawi, Journal of Healthcare Engineering Volume 2022, Article ID 8100697.
  20. Mahesh Kotha, "A Survey on Predicting Uncertainty of Cloud Service Provider Towards Data Integrity and Economic" 2019 IJSRST | Volume 6 | Issue 1 | Print ISSN: 2395-6011 | Online ISSN: 2395-602X.
  21. Kocher T, Holtfreter B, Petersmann A, Eickholz P, Hoffmann T, Kaner D et al (2019) Effect of periodontal treatment on HbA1c among patients with prediabetes. J Dent Res 98(2):171–179.
  22. Meng XH, Huang YX, Rao DP, Zhang Q, Liu Q (2013) Comparison of three data mining models for predicting diabetes or prediabetes by risk factors. Kaohsiung J Med Sci 29(2):93–99.
  23. Sheikhi G, Altınçay H (2016) The cost of type II diabetes mellitus: a machine learning perspective. In: Kyriacou E, Christofides S, Pattichis CS (eds) XIV mediterranean conference on medical and biological engineering and computing 2016. IFMBE proceedings, vol 57. Springer, Cham, pp 818-821.
  24. Mahesh Kotha, "Predict Trustworthiness of Cloud Services Using Linear Regression Model", International Journal of Advanced Science and Technology Vol. 29, No. 4, (2020), pp. 5737 – 5745.
  25. Raja Krishnamoorthi,"A Novel Diabetes Healthcare Disease Prediction Framework Using Machine Learning Techniques", Hindawi, Journal of Healthcare Engineering Volume 2022, Article ID 1684017.
  26. Ravindra Changala, “Automated Health Care Management System Using Big Data Technology” ,Journal of Network Communications and Emerging Technologies (JNCET), Volume 6, Issue 4, April (2016), 2016, pp.37-40,ISSN: 2395-5317, ©EverScience Publications.
  27. Vinoda Reddy, “Recurrent  Feature Grouping and Classification for action model prediction in CBMR”, International  Journal of Data Management and Knowledge Process,  Vol.7, No.5/6, November 2017, pp.  63 74.http://dx.doi.org/10.5121/ijdkp.2017.7605.

Publication Details

Published in : Volume 9 | Issue 3 | May-June 2022
Date of Publication : 2022-06-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 422-433
Manuscript Number : IJSRST229394
Publisher : Technoscience Academy

Print ISSN : 2395-6011, Online ISSN : 2395-602X

Cite This Article :

, Ch .Swetha, Dr. Mahesh Kotha, D Anusha, " A Systematic Review on Machine Learning Algorithms for Diagnosis of Diabetes in Health Care Systems", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 9, Issue 3, pp.422-433, May-June-2022. Available at doi : https://doi.org/10.32628/IJSRST229394    
Journal URL : https://ijsrst.com/IJSRST229394
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