A comprehensive review of machine learning techniques on diabetes detection

Abstract Diabetes mellitus has been an increasing concern owing to its high morbidity, and the average age of individual affected by of individual affected by this disease has now decreased to mid-twenties. Given the high prevalence, it is necessary to address with this problem effectively. Many res...

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Autores principales:	Toshita Sharma, Manan Shah
Formato:	article
Lenguaje:	EN
Publicado:	SpringerOpen 2021
Materias:	Machine learning Deep learning Health care Diabetes detection Drawing. Design. Illustration NC1-1940 Computer applications to medicine. Medical informatics R858-859.7 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/61f20e2546a34a88aed1f779ac92aa29
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spelling	oai:doaj.org-article:61f20e2546a34a88aed1f779ac92aa292021-12-05T12:07:19ZA comprehensive review of machine learning techniques on diabetes detection10.1186/s42492-021-00097-72524-4442https://doaj.org/article/61f20e2546a34a88aed1f779ac92aa292021-12-01T00:00:00Zhttps://doi.org/10.1186/s42492-021-00097-7https://doaj.org/toc/2524-4442Abstract Diabetes mellitus has been an increasing concern owing to its high morbidity, and the average age of individual affected by of individual affected by this disease has now decreased to mid-twenties. Given the high prevalence, it is necessary to address with this problem effectively. Many researchers and doctors have now developed detection techniques based on artificial intelligence to better approach problems that are missed due to human errors. Data mining techniques with algorithms such as - density-based spatial clustering of applications with noise and ordering points to identify the cluster structure, the use of machine vision systems to learn data on facial images, gain better features for model training, and diagnosis via presentation of iridocyclitis for detection of the disease through iris patterns have been deployed by various practitioners. Machine learning classifiers such as support vector machines, logistic regression, and decision trees, have been comparative discussed various authors. Deep learning models such as artificial neural networks and recurrent neural networks have been considered, with primary focus on long short-term memory and convolutional neural network architectures in comparison with other machine learning models. Various parameters such as the root-mean-square error, mean absolute errors, area under curves, and graphs with varying criteria are commonly used. In this study, challenges pertaining to data inadequacy and model deployment are discussed. The future scope of such methods has also been discussed, and new methods are expected to enhance the performance of existing models, allowing them to attain greater insight into the conditions on which the prevalence of the disease depends.Toshita SharmaManan ShahSpringerOpenarticleMachine learningDeep learningHealth careDiabetes detectionDrawing. Design. IllustrationNC1-1940Computer applications to medicine. Medical informaticsR858-859.7Computer softwareQA76.75-76.765ENVisual Computing for Industry, Biomedicine, and Art, Vol 4, Iss 1, Pp 1-16 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Machine learning Deep learning Health care Diabetes detection Drawing. Design. Illustration NC1-1940 Computer applications to medicine. Medical informatics R858-859.7 Computer software QA76.75-76.765
spellingShingle	Machine learning Deep learning Health care Diabetes detection Drawing. Design. Illustration NC1-1940 Computer applications to medicine. Medical informatics R858-859.7 Computer software QA76.75-76.765 Toshita Sharma Manan Shah A comprehensive review of machine learning techniques on diabetes detection
description	Abstract Diabetes mellitus has been an increasing concern owing to its high morbidity, and the average age of individual affected by of individual affected by this disease has now decreased to mid-twenties. Given the high prevalence, it is necessary to address with this problem effectively. Many researchers and doctors have now developed detection techniques based on artificial intelligence to better approach problems that are missed due to human errors. Data mining techniques with algorithms such as - density-based spatial clustering of applications with noise and ordering points to identify the cluster structure, the use of machine vision systems to learn data on facial images, gain better features for model training, and diagnosis via presentation of iridocyclitis for detection of the disease through iris patterns have been deployed by various practitioners. Machine learning classifiers such as support vector machines, logistic regression, and decision trees, have been comparative discussed various authors. Deep learning models such as artificial neural networks and recurrent neural networks have been considered, with primary focus on long short-term memory and convolutional neural network architectures in comparison with other machine learning models. Various parameters such as the root-mean-square error, mean absolute errors, area under curves, and graphs with varying criteria are commonly used. In this study, challenges pertaining to data inadequacy and model deployment are discussed. The future scope of such methods has also been discussed, and new methods are expected to enhance the performance of existing models, allowing them to attain greater insight into the conditions on which the prevalence of the disease depends.
format	article
author	Toshita Sharma Manan Shah
author_facet	Toshita Sharma Manan Shah
author_sort	Toshita Sharma
title	A comprehensive review of machine learning techniques on diabetes detection
title_short	A comprehensive review of machine learning techniques on diabetes detection
title_full	A comprehensive review of machine learning techniques on diabetes detection
title_fullStr	A comprehensive review of machine learning techniques on diabetes detection
title_full_unstemmed	A comprehensive review of machine learning techniques on diabetes detection
title_sort	comprehensive review of machine learning techniques on diabetes detection
publisher	SpringerOpen
publishDate	2021
url	https://doaj.org/article/61f20e2546a34a88aed1f779ac92aa29
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A comprehensive review of machine learning techniques on diabetes detection

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