Skip to main content
SpringerLink
Log in

Machine Learning and Diabetes

  • Chapter
  • First Online:
Machine Learning in Biological Sciences

  • 1023 Accesses

Abstract

Diabetes mellitus is a chronic metabolic disease revealing high blood glucose levels that affect the body with associated complications and can cause detrimental fatal effects and is a cause of concern globally. The disease lacks complete cure and cannot be prevented and early diagnosis and proper care and management of the disease is the only hope to prevent mortality. Machine learning tools are being employed in the study of management, risk prediction, diagnosis and prognosis of the disease, and study of its associated complicacies. We discuss in this chapter the different domains of application of machine learning in understanding the biology of diabetes and proper prevention and prediction.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Abbreviations

AD:

AdaBoost

AMD:

Age related macular degeneration

BG:

Blood glucose

BMI:

Body mass index

CVD:

Cardiovascular disease

DLS:

Deep learning system

DM:

Diabetes mellitus

DR:

Diabetic retinopathy

EHR:

Electronic health record

GDM:

Gestational diabetes mellitus

GTB:

Gradient tree boosting

GV:

Glycemic variability

GWAS:

Genome wide association studies

HbA1c:

Glycated hemoglobin

HF:

Heart failure

miRNA:

MicroRNAs

ML:

Machine learning

MLP:

Multilayer perceptron

PheWAS:

Phenome-wide association study

PWV:

Pulse wave velocity

SVM:

Support vector machine

T1DM:

Type 1 diabetes mellitus

T2DM:

Type 2 diabetes mellitus

WHO:

World Health Organization

References

  • Bhat V, Tazari M, Watt KD, Bhat M (2018) New-onset diabetes and preexisting diabetes are associated with comparable reduction in long-term survival after liver transplant: a machine learning approach. Mayo Clin Proc 93(12):1794–1802

    Article  Google Scholar 

  • Broome DT, Hilton CB, Mehta N (2020) Policy implications of artificial intelligence and machine learning in diabetes management. Curr Diab Rep 20(2):5

    Article  Google Scholar 

  • Contreras I, Vehi J (2018) Artificial intelligence for diabetes management and decision support: literature review. J Med Internet Res 20(5):e10775

    Article  Google Scholar 

  • Dagliati A, Marini S, Sacchi L, Cogni G, Teliti M, Tibollo V, De Cata P, Chiovato L, Bellazzi R (2018) Machine learning methods to predict diabetes complications. J Diabetes Sci Technol 12(2):295–302

    Article  Google Scholar 

  • Dinh A, Miertschin S, Young A, Mohanty SD (2019) A data-driven approach to predicting diabetes and cardiovascular disease with machine learning. BMC Med Inform Decis Mak 19(1):211

    Article  Google Scholar 

  • Garcia-Carretero R, Vigil-Medina L, Barquero-Perez O, Ramos-Lopez J (2019) Pulse wave velocity and machine learning to predict cardiovascular outcomes in prediabetic and diabetic populations. J Med Syst 44(1):16

    Article  Google Scholar 

  • Gruson D, Helleputte T, Rousseau P, Gruson D (2019) Data science, artificial intelligence, and machine learning: opportunities for laboratory medicine and the value of positive regulation. Clin Biochem 69:1–7

    Article  Google Scholar 

  • Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J, Kim R, Raman R, Nelson PC, Mega JL, Webster DR (2016) Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22):2402–2410

    Article  Google Scholar 

  • Kavakiotis I, Tsave O, Salifoglou A, Maglaveras N, Vlahavas I, Chouvarda I (2017) Machine learning and data mining methods in diabetes research. Comput Struct Biotechnol J 15:104–116

    Article  Google Scholar 

  • Maniruzzaman M, Rahman MJ, Al-MehediHasan M, Suri HS, Abedin MM, El-Baz A, Suri JS (2018) Accurate diabetes risk stratification using machine learning: role of missing value and outliers. J Med Syst 42(5):92

    Article  Google Scholar 

  • Nagaraj SB, Sidorenkov G, van Boven JFM, Denig P (2019) Predicting short- and long-term glycated haemoglobin response after insulin initiation in patients with type 2 diabetes mellitus using machine-learning algorithms. Diabetes Obes Metab 21(12):2704–2711

    Article  CAS  Google Scholar 

  • Nguyen BP, Pham HN, Tran H, Nghiem N, Nguyen QH, Do TTT, Tran CT, Simpson CR (2019) Predicting the onset of type 2 diabetes using wide and deep learning with electronic health records. Comput Methods Prog Biomed 182:105055

    Article  Google Scholar 

  • Rigla M, García-Sáez G, Pons B, Hernando ME (2018) Artificial intelligence methodologies and their application to diabetes. J Diabetes Sci Technol 12(2):303–310

    Article  Google Scholar 

  • Samant P, Agarwal R (2018) Machine learning techniques for medical diagnosis of diabetes using iris images. Comput Methods Prog Biomed 157:121–128

    Article  Google Scholar 

  • Segar MW, Vaduganathan M, Patel KV, McGuire DK, Butler J, Fonarow GC, Basit M, Kannan V, Grodin JL, Everett B, Willett D, Berry J, Pandey A (2019) Machine learning to predict the risk of incident heart failure hospitalization among patients with diabetes: the WATCH-DM risk score. Diabetes Care 42(12):2298–2306

    Article  Google Scholar 

  • Seo W, Lee YB, Lee S, Jin SM, Park SM (2019) A machine-learning approach to predict postprandial hypoglycemia. BMC Med Inform Decis Mak 19(1):210

    Article  Google Scholar 

  • Ting DSW, Cheung CY, Lim G, Tan GSW, Quang ND, Gan A, Hamzah H, Garcia-Franco R, San Yeo IY, Lee SY, Wong EYM, Sabanayagam C, Baskaran M, Ibrahim F, Tan NC, Finkelstein EA, Lamoureux EL, Wong IY, Bressler NM, Sivaprasad S, Varma R, Jonas JB, He MG, Cheng CY, Cheung GCM, Aung T, Hsu W, Lee ML, Wong TY (2017) Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. JAMA 318(22):2211–2223

    Article  Google Scholar 

  • Tsao HY, Chan PY, Su EC (2018) Predicting diabetic retinopathy and identifying interpretable biomedical features using machine learning algorithms. BMC Bioinf 19(Suppl 9):283

    Article  Google Scholar 

  • Woldaregay AZ, Ã…rsand E, Walderhaug S, Albers D, Mamykina L, Botsis T, Hartvigsen G (2019a) Data-driven modeling and prediction of blood glucose dynamics: machine learning applications in type 1 diabetes. Artif Intell Med 98:109–134

    Article  Google Scholar 

  • Woldaregay AZ, Ã…rsand E, Botsis T, Albers D, Mamykina L, Hartvigsen G (2019b) Data-driven blood glucose pattern classification and anomalies detection: machine-learning applications in type 1 diabetes. J Med Internet Res 21(5):e11030

    Article  Google Scholar 

  • Xiong XL, Zhang RX, Bi Y, Zhou WH, Yu Y, Zhu DL (2019) Machine learning models in type 2 diabetes risk prediction: results from a cross-sectional retrospective study in Chinese adults. Curr Med Sci 39(4):582–588

    Article  Google Scholar 

  • Yoffe L, Polsky A, Gilam A, Raff C, Mecacci F, Ognibene A, Crispi F, Gratacós E, Kanety H, Mazaki-Tovi S, Shomron N, Hod M (2019) Early diagnosis of gestational diabetes mellitus using circulating microRNAs. Eur J Endocrinol 181(5):565–577

    Article  Google Scholar 

  • Zheng T, Xie W, Xu L, He X, Zhang Y, You M, Yang G, Chen Y (2017) A machine learning-based framework to identify type 2 diabetes through electronic health records. Int J Med Inform 97:120–127

    Article  Google Scholar 

  • Zou Q, Qu K, Luo Y, Yin D, Ju Y, Tang H (2018) Predicting diabetes mellitus with machine learning techniques. Front Genet 9:515

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Biological Sciences, National Institute of Science Education and Research (NISER), Bhubaneswar, Odisha, India

    Shyamasree Ghosh

  2. Heathcare, Data Core Systems, Philadelphia, PA, USA

    Rathi Dasgupta

Authors
  1. Shyamasree Ghosh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rathi Dasgupta
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Ghosh, S., Dasgupta, R. (2022). Machine Learning and Diabetes. In: Machine Learning in Biological Sciences. Springer, Singapore. https://doi.org/10.1007/978-981-16-8881-2_14

Download citation

Publish with us

Policies and ethics

Search

Navigation