A review on current advances in machine learning based diabetes prediction

•Overview of diverse machine learning techniques that can help in rapid automatic prediction of diabetes.•Data mining techniques for classification of efficient and effective diabetes diagnoses.•Better prediction of disease and improve in understanding the pattern of diabetes.•Existing methods need...

Full description

Saved in:
Bibliographic Details
Published in:Primary care diabetes Vol. 15; no. 3; pp. 435 - 443
Main Authors: Jaiswal, Varun, Negi, Anjli, Pal, Tarun
Format: Journal Article
Language:English
Published: England Elsevier Ltd 01.06.2021
Subjects:
Apriori Algorithm
Artificial neural network
Back propagation algorithm
Bayesian network
Diabetes
Machine learning
SVM
Apriori Algorithm
Machine learning
Diabetes
Artificial neural network
SVM
Back propagation algorithm
Bayesian network
ISSN:1751-9918, 1878-0210, 1878-0210
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•Overview of diverse machine learning techniques that can help in rapid automatic prediction of diabetes.•Data mining techniques for classification of efficient and effective diabetes diagnoses.•Better prediction of disease and improve in understanding the pattern of diabetes.•Existing methods need to be improvised tested on different datasets or peoples from different countries. Diabetes is a metabolic disorder comprising of high glucose level in blood over a prolonged period in the body as it is not capable of using it properly. The severe complications associated with diabetes include diabetic ketoacidosis, nonketotic hypersmolar coma, cardiovascular disease, stroke, chronic renal failure, retinal damage and foot ulcers. There is a huge increase in the number of patients with diabetes globally and it is considered a major health problem worldwide. Early diagnosis of diabetes is helpful for treatment and reduces the chance of severe complications associated with it. Machine learning algorithms (such as ANN, SVM, Naive Bayes, PLS-DA and deep learning) and data mining techniques are used for detecting interesting patterns for diagnosing and treatment of disease. Current computational methods for diabetes diagnosis have some limitations and are not tested on different datasets or peoples from different countries which limits the practical use of prediction methods. This paper is an effort to summarize the majority of the literature concerned with machine learning and data mining techniques applied for the prediction of diabetes and associated challenges. This report would be helpful for better prediction of disease and improve in understanding the pattern of diabetes. Consequently, the report would be helpful for treatment and reduce risk of other complications of diabetes.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
ObjectType-Review-3
content type line 23
ISSN:1751-9918
1878-0210
1878-0210
DOI:10.1016/j.pcd.2021.02.005