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Current Diabetes Reviews

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ISSN (Print): 1573-3998
ISSN (Online): 1875-6417

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Review Article

A Review of Statistical and Machine Learning Techniques for Microvascular Complications in Type 2 Diabetes

Author(s): Nitigya Sambyal*, Poonam Saini and Rupali Syal

Volume 17, Issue 2, 2021

Published on: 10 May, 2020

Page: [143 - 155] Pages: 13

DOI: 10.2174/1573399816666200511003357

Price: $65

Abstract

Background and Introduction: Diabetes mellitus is a metabolic disorder that has emerged as a serious public health issue worldwide. According to the World Health Organization (WHO), without interventions, the number of diabetic incidences is expected to be at least 629 million by 2045. Uncontrolled diabetes gradually leads to progressive damage to eyes, heart, kidneys, blood vessels, and nerves.

Methods: The paper presents a critical review of existing statistical and Artificial Intelligence (AI) based machine learning techniques with respect to DM complications, mainly retinopathy, neuropathy, and nephropathy. The statistical and machine learning analytic techniques are used to structure the subsequent content review.

Results: It has been observed that statistical analysis can help only in inferential and descriptive analysis whereas, AI-based machine learning models can even provide actionable prediction models for faster and accurate diagnosis of complications associated with DM.

Conclusion: The integration of AI-based analytics techniques, like machine learning and deep learning in clinical medicine, will result in improved disease management through faster disease detection and cost reduction for the treatment.

Keywords: Microvascular complications, retinopathy, nephropathy, neuropathy, machine learning, statistical analysis.

[1]
Kumar S, Singh M. Big data analytics for healthcare industry: impact, applications, and tools. Big Data Min Anal 2019; 2: 48-57.
[http://dx.doi.org/10.26599/BDMA.2018.9020031]
[2]
Kavakiotis I, Tsave O, Salifoglou A, Maglaveras N, Vlahavas I, Chouvarda I. Machine learning and data mining methods in diabetes research. Comput Struct Biotechnol J 2017; 15: 104-16.
[http://dx.doi.org/10.1016/j.csbj.2016.12.005] [PMID: 28138367]
[3]
World Health Organization. Classification of diabetes mellitus 2019.
[4]
World Health Organization. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: Diagnosis and Classification of Diabetes Mellitus 1999; 1-66
[5]
Engelgau MiM Venkat Narayan KM. Herman WH. Screening for type 2 diabetes. Diabetes Care 2000; 23: 1563-80.
[http://dx.doi.org/10.2337/diacare.23.10.1563]
[6]
Campagna D, Alamo A, Di Pino A, et al. Smoking and diabetes: dangerous liaisons and confusing relationships. Diabetol Metab Syndr 2019; 11: 85.
[http://dx.doi.org/10.1186/s13098-019-0482-2] [PMID: 31666811]
[7]
Sami W, Ansari T, Butt NS, Hamid MRA, Hamid A. Effect of diet on type 2 diabetes mellitus: A review. Int J Health Sci (Qassim) 2017; 11(2): 65-71.
[PMID: 28539866]
[8]
Cappon G, Acciaroli G, Vettoretti M, Facchinetti A, Sparacino G. Wearable continuous glucose monitoring sensors: a revolution in diabetes treatment. Electronics (Basel) 2017; 6: 1-16.
[9]
Fowler M icheal J. Microvascular and macrovascular complications of diabetes. Clin Diabetes 2008; 26: 77-82.
[http://dx.doi.org/10.2337/diaclin.26.2.77]
[10]
Lebovitz HE, Banerji MA. Ketosis-prone diabetes (flatbush diabetes): an emerging worldwide clinically important entity. Curr Diab Rep 2018; 18(11): 120.
[http://dx.doi.org/10.1007/s11892-018-1075-4] [PMID: 30280274]
[11]
Pieralice S, Pozzilli P. Latent autoimmune diabetes in adults: a review on clinical implications and management. Diabetes Metab J 2018; 42(6): 451-64.
[http://dx.doi.org/10.4093/dmj.2018.0190] [PMID: 30565440]
[12]
Fong DS, Aiello LP, Ferris FL III, Klein R. Diabetic retinopathy. Diabetes Care 2004; 27(10): 2540-53.
[http://dx.doi.org/10.2337/diacare.27.10.2540] [PMID: 15451934]
[13]
American Diabetes Association. Standards of medical care in diabetes--2007. Diabetes Care 2007; 30(Suppl. 1): S4-S41.
[http://dx.doi.org/10.2337/dc07-S004] [PMID: 17192377]
[14]
Boulton AJM, Vinik AI, Arezzo JC, et al. American Diabetes Association. Diabetic neuropathies: a statement by the American Diabetes Association. Diabetes Care 2005; 28(4): 956-62.
[http://dx.doi.org/10.2337/diacare.28.4.956] [PMID: 15793206]
[15]
Han D, Wang S, Jiang C, et al. Trends in biomedical informatics: automated topic analysis of JAMIA articles. J Am Med Inform Assoc 2015; 22(6): 1153-63.
[http://dx.doi.org/10.1093/jamia/ocv157] [PMID: 26555018]
[16]
Norvig P, Russell SJ. Artificial intelligence : a modern approach. United States: Prentice Hall 2009.
[17]
Alpaydin E. Introduction to machine learning. United States: MIT Press 2004.
[18]
Malhotra M, Lal AK, Singh VP, Malik PK, Arya V, Agarwal AK. Prevalence of diabetic retinopathy in type 2 diabetics and its correlation with various clinical and metabolic factors. Int J Diabetes Dev Ctries 2014; 35: 303-9.
[http://dx.doi.org/10.1007/s13410-013-0183-6]
[19]
Sigamani V, Kanagaraj ND, Rajagantham VK. 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. J Evol Med Dent Sci 2017; 6: 4761-4.
[http://dx.doi.org/10.14260/Jemds/2017/1031]
[20]
Man REK, Fenwick EK, Gan ATL, et al. Association between perceived barriers to diabetes self-management and diabetic retinopathy in asian patients with type 2 diabetes. JAMA Ophthalmol 2017; 135(12): 1387-93.
[http://dx.doi.org/10.1001/jamaophthalmol.2017.4888] [PMID: 29145552]
[21]
Alam U, Riley DR, Jugdey RS, et al. Diabetic Neuropathy and Gait: A Review. Diabetes Ther 2017; 8(6): 1253-64.
[http://dx.doi.org/10.1007/s13300-017-0295-y] [PMID: 28864841]
[22]
Shende S, Baig M, Doifode S. Evaluation of efficacy and safety of epalrestat (150 mg) compared to epalrestat (50 mg) in patients suffering from diabetic peripheral neuropathy. J Clin Diagn Res 2018; 15-9.
[http://dx.doi.org/10.7860/JCDR/2018/32716.11444]
[23]
Sandesara PB, O’Neal WT, Kelli HM, et al. The prognostic significance of diabetes and microvascular complications in patients with heart failure with preserved ejection fraction. Diabetes Care 2018; 41(1): 150-5.
[http://dx.doi.org/10.2337/dc17-0755] [PMID: 29051160]
[24]
Roberts TJ, Burns AT, MacIsaac RJ, MacIsaac AI, Prior DL, La Gerche A. Diagnosis and significance of pulmonary microvascular disease in diabetes. Diabetes Care 2018; 41(4): 854-61.
[http://dx.doi.org/10.2337/dc17-1904] [PMID: 29351959]
[25]
Park S, Moon S, Lee K, Park IB, Lee DH, Nam S. Urinary and Blood MicroRNA-126 and -770 are Potential Noninvasive Biomarker Candidates for Diabetic Nephropathy: a Meta-Analysis. Cell Physiol Biochem 2018; 46(4): 1331-40.
[http://dx.doi.org/10.1159/000489148] [PMID: 29689545]
[26]
Bourne RRA, Stevens GA, White RA, et al. Vision Loss Expert Group. Causes of vision loss worldwide, 1990-2010: a systematic analysis. Lancet Glob Health 2013; 1(6): e339-49.
[http://dx.doi.org/10.1016/S2214-109X(13)70113-X] [PMID: 25104599]
[27]
Zhang W, Liu H, Al-Shabrawey M, Caldwell RW, Caldwell RB. Inflammation and diabetic retinal microvascular complications. J Cardiovasc Dis Res 2011; 2(2): 96-103.
[http://dx.doi.org/10.4103/0975-3583.83035] [PMID: 21814413]
[28]
Roychowdhury S, Koozekanani DD, Parhi KK. DREAM: diabetic retinopathy analysis using machine learning. IEEE J Biomed Health Inform 2014; 18(5): 1717-28.
[http://dx.doi.org/10.1109/JBHI.2013.2294635] [PMID: 25192577]
[29]
Pal R, Poray J, Sen M. Application of machine learning algorithms on diabetic retinopathy. 2nd IEEE Int Conf Recent Trends Electron Inf Commun Technol India IEEE 2013.
[30]
Dutta S, Manideep BC, Basha SM, Caytiles RD, Iyengar NCSN. Classification of diabetic retinopathy images by using deep learning models. Int J Grid Distrib Comput 2018; 11: 99-106.
[http://dx.doi.org/10.14257/ijgdc.2018.11.1.09]
[31]
Gargeya R, Leng T. Automated identification of diabetic retinopathy using deep learning. Ophthalmology 2017; 124(7): 962-9.
[http://dx.doi.org/10.1016/j.ophtha.2017.02.008] [PMID: 28359545]
[32]
Shanthi T, Sabeenian RS. Modified alexnet architecture for classification of diabetic retinopathy images. Comput Electr Eng 2019; 76: 56-64.
[http://dx.doi.org/10.1016/j.compeleceng.2019.03.004]
[33]
Khojasteh P, Passos Júnior LA, Carvalho T, et al. Exudate detection in fundus images using deeply-learnable features. Comput Biol Med 2019; 104: 62-9.
[http://dx.doi.org/10.1016/j.compbiomed.2018.10.031] [PMID: 30439600]
[34]
Abawajy J, Kelarev A, Chowdhury M, Stranieri A, Jelinek HF. Predicting cardiac autonomic neuropathy category for diabetic data with missing values. Comput Biol Med 2013; 43(10): 1328-33.
[http://dx.doi.org/10.1016/j.compbiomed.2013.07.002] [PMID: 24034723]
[35]
Jelinek HF, Cornforth DJ. Machine learning methods for automated detection of severe diabetic neuropathy. J Diabet Complicat Med 2016; 1: 1-7.
[http://dx.doi.org/10.4172/2475-3211.1000108]
[36]
Cho BH, Yu H, Kim KW, Kim TH, Kim IY, Kim SI. Application of irregular and unbalanced data to predict diabetic nephropathy using visualization and feature selection methods. Artif Intell Med 2008; 42(1): 37-53.
[http://dx.doi.org/10.1016/j.artmed.2007.09.005] [PMID: 17997291]

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