Microvascular Complications in Type-2 Diabetes: A Review of Statistical Techniques and Machine Learning Models

Abstract

Type 2 Diabetes (T2D) has become a serious concern as it contributes to 90% of the total diabetic population. The high glucose levels in diabetic patients result in damage to eyes, kidneys and nerves collectively known as microvascular complications. The paper presents a critical review of existing statistical and machine learning models with respect to such complications namely retinopathy, neuropathy and nephropathy. The statistical tests like chi-square, odds ratio, t-test, ANOVA enables inferential and descriptive analysis. On the other side, machine learning models like support vector machines, k-nearest neighbour, deep neural network, naive bayes also assures predictive analytics. It is worth mentioning that both analytics can reverse impaired glucose regulation early in its course resulting in the prevention of long-term complications associated with T2D. Despite variation in both analytic techniques, their integration in medical health practices can assist patients to effectively adapt a healthy lifestyle and reduce significant healthcare overheads for an individual's family as well as society.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3

Notes

  1. 1.

    Confidence Interval, (CI), is an interval estimate computed from the statistics of observed data that contains the true value of the unknown parameter.

References

  1. 1.

    Shankar, K., Sait, A. R. W., Gupta, D., Lakshmanaprabu, S. K., Khanna, A., & Pandey, H. M. (2020). Automated detection and classification of fundus diabetic retinopathy images using synergic deep learning model. Pattern Recognition Letters,133, 210–216.

    Article  Google Scholar 

  2. 2.

    MiM, E., Venkat Narayan, K. M., & Herman, W. H. (2000). Screening for type 2 diabetes. Diabetes Care,23, 1563–1580.

    Article  Google Scholar 

  3. 3.

    Campagna, D., Alamo, A., Di Pino, A., Russo, C., Calogero, A. E., Purrello, F., et al. (2019). Smoking and diabetes: Dangerous liaisons and confusing relationships. Diabetology and Metabolic Syndrome,11(85), 1–12.

    Google Scholar 

  4. 4.

    Sami, W., Ansari, T., Butt, N. S., Rashid, M., & Hamid, A. (2017). Effect of diet on type 2 diabetes mellitus: A review. International Journal of Health Sciences,11(2), 65–71.

    Google Scholar 

  5. 5.

    World Health Organization. (1999). Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: Diagnosis and Classification of Diabetes Mellitus,1999, 1–66.

    Google Scholar 

  6. 6.

    Kavakiotis, I., Tsave, O., Salifoglou, A., Maglaveras, N., Vlahavas, I., & Chouvarda, I. (2017). Machine learning and data mining methods in diabetes research. Computational and Structural Biotechnology Journal,15, 104–116.

    Article  Google Scholar 

  7. 7.

    World Health Organization. (2019). Classification of diabetes mellitus.

  8. 8.

    Saleh, E., Błaszczyński, J., Moreno, A., Valls, A., Romero-Aroca, P., de la Riva-Fernández, S., et al. (2007). Learning ensemble classifiers for diabetic retinopathy assessment. Artificial Intelligence in Medicine,85, 50–63.

    Article  Google Scholar 

  9. 9.

    Bansal, V., Kalita, J., & Misra, U. K. (2006). Diabetic neuropathy. Postgraduate Medical Journal,82, 95–100.

    Article  Google Scholar 

  10. 10.

    Fowler, M., & Icheal, J. (2008). Microvascular and macrovascular complications of diabetes. Clinical Diabetes,26, 77–82.

    Article  Google Scholar 

  11. 11.

    American Diabetes Association. (2007). Standards of medical care in diabetes. Diabetes Care,30, S4–41.

    Article  Google Scholar 

  12. 12.

    Sambyal, N., Saini, P., Syal, R. (2020). A review of statistical and machine learning techniques for microvascular complications in Type 2 diabetes. Current Diabetes Reviews (accepted).

  13. 13.

    Piatetsky-Shapiro, G., & Gregory, K. (2000). Knowledge discovery in databases. ACM SIGKDD Explorations Newsletters,1, 59.

    MATH  Article  Google Scholar 

  14. 14.

    Han, D., Wang, S., Jiang, C., Jiang, X., Kim, H.-E., Sun, J., et al. (2015). Trends in biomedical informatics: automated topic analysis of JAMIA articles. Journal of American Medical Informatics Association,22, 1153–1163.

    Article  Google Scholar 

  15. 15.

    Norvig, P., & Russell, S. J. (2009). Artificial intelligence: A modern approach. New York: Prentice Hall.

    Google Scholar 

  16. 16.

    Malhotra, M., Lal, A. K., Singh, V. P., Malik, P. K., Arya, V., & Agarwal, A. K. (2014). Prevalence of diabetic retinopathy in type 2 diabetics and its correlation with various clinical and metabolic factors. International Journal of Diabetes in Developing Countries,35, 303–309.

    Article  Google Scholar 

  17. 17.

    Sigamani, V., Kanagaraj, N. D., & Rajagantham, V. K. (2017). The association between poor glycaemic control (HbA1C levels >= 7%) and higher incidence of diabetic retinopathy in small group of type 2 diabetes mellitus patients attending mahatma gandhi memorial government hospital, trichy. Journal of Evolution of Medical and Dental Sciences,6(66), 4761–4764.

    Article  Google Scholar 

  18. 18.

    Ji, H., Yi, Q., Chen, L., Wong, L., Liu, Y., Xu, G., et al. (2020). Circulating miR-3197 and miR-2116-5p as novel biomarkers for diabetic retinopathy. Clinica Chimica Acta,501, 147–153.

    Article  Google Scholar 

  19. 19.

    Lv, X., Ran, X., Chen, X., Luo, T., Hu, J., Wang, Y., et al. (2020). Early-onset type 2 diabetes: A high-risk factor for proliferative diabetic retinopathy(PDR) in patients with microalbuminuria. Medicine,99(19), 1–5.

    Article  Google Scholar 

  20. 20.

    Alam, U., Riley, D. R., Jugdey, R. S., Azmi, S., Rajbhandari, S., D’Août, K., et al. (2017). Diabetic neuropathy and gait: A review. Diabetes Therepy,8, 1253–1264.

    Article  Google Scholar 

  21. 21.

    Shende, S., Baig, M., & Doifode, S. (2018). Evaluation of efficacy and safety of epalrestat (150 mg) compared to epalrestat (50 mg) in patients suffering from diabetic peripheral neuropathy. Journal of Clinical and Diagnostic Research,12(4), 15–19.

    Google Scholar 

  22. 22.

    Joshi, D., Khan, M. A., & Singh, A. (2020). A clinical study of the association and risk factor for lower limb neuropathy in patients with diabetic retinopathy. Journal of Family Medicine and Primary Care,9(4), 1891–1895.

    Article  Google Scholar 

  23. 23.

    Eleftheriadou, I., Dimitrakopoulou, N., Kafasi, N., Tentolouris, A., Dimitrakopoulou, A., Anastasiou, I. A., et al. (2019). Endothelial progenitor cells and peripheral neuropathy in subjects with type 2 diabetes mellitus. Journal of Diabetes and its Complications,34, 1–9.

    Google Scholar 

  24. 24.

    Sandesara, P. B., O’Neal, W. T., Kelli, H. M., Samman-Tahhan, A., Hammadah, M., Quyyumi, A. A., et al. (2017). The prognostic significance of diabetes and microvascular complications in patients with heart failure with preserved ejection fraction. Diabetes Care,41(1), 150–155.

    Article  Google Scholar 

  25. 25.

    Park, S., Moon, S., Lee, K., Park, I. B., Lee, D. H., & Nam, S. (2018). Urinary and blood MicroRNA-126 and -770 are potential noninvasive biomarker candidates for diabetic nephropathy: A meta-analysis. Cellular Physiology and Biochemistry,46(4), 1331–1340.

    Article  Google Scholar 

  26. 26.

    Mistry, K. N., Dabhi, B. K., & Joshi, B. B. (2020). Evaluation of oxidative stress biomarkers and inflammation in pathogenesis of diabetes and diabetic nephropathy. Indian Journal of Biochemistry and Biophysics,57, 45–50.

    Google Scholar 

  27. 27.

    Khynriam, D., & Prasad, S. B. (2001). Hematotoxicity and blood glutathione levels after cisplatin treatment of tumor-bearing mice. Cell Biology and Toxicology,17, 357–370.

    Article  Google Scholar 

  28. 28.

    Marbut, M. M., Majeed, B. M., Rahim, S. M., & Yuusif, M. N. (2009). Estimation of malondialdehyde as oxidative factor & glutathione as early detectors of hypertensive pregnant women. Tikrit Medical Journal,15(2), 63–69.

    Google Scholar 

  29. 29.

    Góth, L. (1991). A simple method for determination of serum catalase activity and revision of reference range. Clinica Chimica Acta,196, 143–151.

    Article  Google Scholar 

  30. 30.

    Madesh, M., & Balasubramanian, K. A. (1998). A microtiter plate assay for superoxide using MTT reduction method. Indian Journal of Biochemistry and Biophysics,34(6), 535–539.

    Google Scholar 

  31. 31.

    Parmar, D., Bhattacharya, N., Kannan, S., Vadivel, S., Pandey, G. K., Ghanate, A., et al. (2020). Plausible diagnostic value of urinary isomeric dimethylarginine ratio for diabetic nephropathy. Scientific Reports,10, 1–7.

    Article  Google Scholar 

  32. 32.

    Roychowdhury, S., Koozekanani, D. D., & Parhi, K. K. (2014). DREAM: Diabetic retinopathy analysis using machine learning. IEEE Journal of Biomedical and Health Informatics,18(5), 1717–1728.

    Article  Google Scholar 

  33. 33.

    Pal, R., Poray, J., Sen, M. (2013). Application of machine learning algorithms on diabetic retinopathy. In 2nd IEEE International Conference on Recent Trends in Electronics Information and Communication Technology, pp. 2046–2051.

  34. 34.

    Huda, S.M.A., Ila, I.J., Sarder, S., Shamsujjoha, M., Ali, M.N.Y. (2019). An improved approach for detection of diabetic retinopathy using feature importance and machine learning algorithms. In 7th International Conference on Smart Computing & Communications, IEEE, pp 1–5.

  35. 35.

    Brata Chanda, P., & Sarkar, S. K. (2019). Automatic identification of blood vessels, exaudates and abnormalities in retinal images for diabetic retinopathy analysis. International Conference on Advancements in Computing & Management,2019, 1–9.

    Google Scholar 

  36. 36.

    Deepa, R., & Narayanan, N. K. (2020). Detection of microaneurysm in retina image using machine learning approach. International Conference on Innovative Trends in Information Technology,2020, 1–5.

    Google Scholar 

  37. 37.

    Reddy, G. T., Bhattacharya, S., Ramakrishnan, S. S., Chowdhary, C., Hakak, S., Kaluri, R., et al. (2020). An ensemble based machine learning model for diabetic retinopathy classification. International Conference on Emerging Trends on Information Technology and Engineering,2020, 1–6.

    Google Scholar 

  38. 38.

    Abawajy, J., Kelarev, A., Chowdhury, M., Stranieri, A., & Jelinek, H. F. (2013). Predicting cardiac autonomic neuropathy category for diabetic data with missing values. Computers in Biology and Medicine,43, 1328–1333.

    Article  Google Scholar 

  39. 39.

    Jelinek, H. F., & Cornforth, D. J. (2016). Machine learning methods for automated detection of severe diabetic neuropathy. Journal of Diabetic Complications & Medicine,1(2), 1–7.

    Article  Google Scholar 

  40. 40.

    Cho, B. H., Yu, H., Kim, K. W., Kim, T. H., Kim, I. Y., & Kim, S. I. (2008). Application of irregular and unbalanced data to predict diabetic nephropathy using visualization and feature selection methods. Artificial Intelligence in Medicine,42, 37–53.

    Article  Google Scholar 

  41. 41.

    Makino, M., Yoshimoto, R., Ono, M., Itoko, T., Katsuki, T., Koseki, A., et al. (2019). Artificial intelligence predicts the progression of diabetic kidney disease using big data machine learning. Scientific Reports,9, 1–9.

    Article  Google Scholar 

  42. 42.

    Gargeya, R., & Leng, T. (2017). Automated identification of diabetic retinopathy using deep learning. American Journal of Ophthalmology,124, 962–969.

    Google Scholar 

  43. 43.

    Dutta, S., Manideep, B. C., Basha, S. M., Caytiles, R. D., & Iyengar, N. C. S. N. (2018). Classification of diabetic retinopathy images by using deep learning models. International Journal of Grid and Distributed Computing,11(1), 99–106.

    Article  Google Scholar 

  44. 44.

    Khojasteh, P., Passos Júnior, L. A., Carvalho, T., Rezende, E., Aliahmad, B., Papa, J. P., et al. (2019). Exudate detection in fundus images using deeply-learnable features. Computers in Biology and Medicine,104, 62–69.

    Article  Google Scholar 

  45. 45.

    Wan, S., Liang, Y., & Zhang, Y. (2018). Deep convolutional neural networks for diabetic retinopathy detection by image classification. Computers and Electrical Engineering,72, 274–282.

    Article  Google Scholar 

  46. 46.

    Rubini, S.S., Nithil Saai, R., Kunthavai, A., Sharma, A. (2019). Deep convolutional neural network- based diabetic retinopathy detection in digital fundus images. In Soft computing and signal Processing (pp. 201–9). Singapore: Springer.

  47. 47.

    Shanthi, T., & Sabeenian, R. S. (2019). Modified alexnet architecture for classification of diabetic retinopathy images. Computers and Electrical Engineering,76, 56–64.

    Article  Google Scholar 

  48. 48.

    Mateen, M., Wen, J., Nasrullah, N., Sun, S., & Hayat, S. (2020). Exudate detection for diabetic retinopathy using pretrained convolutional neural networks. Complexity,2020, 1–11.

    Article  Google Scholar 

  49. 49.

    Scarpa, F., Colonna, A., & Ruggeri, A. (2019). Multiple-image deep learning analysis for neuropathy detection in corneal nerve images. Cornea,39(3), 342–347.

    Article  Google Scholar 

  50. 50.

    Mahalakshmi, S., Menaka, P., & Rajkumar, R. S. (2019). Classification of chronic kidney disease stages in diabetic patients. International Journal of Research and Analytical Reviews,6(1), 389–395.

    Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Nitigya Sambyal.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Sambyal, N., Saini, P. & Syal, R. Microvascular Complications in Type-2 Diabetes: A Review of Statistical Techniques and Machine Learning Models. Wireless Pers Commun (2020). https://doi.org/10.1007/s11277-020-07552-3

Download citation

Keywords

  • Microvascular complications
  • T2D
  • Retinopathy
  • Neuropathy
  • Nephropathy
  • Statistical analysis
  • Machine learning
  • Deep learning