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Diabetes subtypes classification for personalized health care: A review

Healthcare is evolving from standard to personalized, driven by the patients’ needs. Personalized healthcare is a medical model based on genetics, genomics, and other biological information that helps to predict risk for disease. To date, machine learning and data mining are the fastest-growing heal...

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Main Authors: Omar, Nashuha, Nazirun, Nisha Nadhira, Vijayam, Bhuwaneswaran, Abdul Wahab, Asnida, Ahmad Bahuri, Hana
Format: Article
Published: Springer Nature 2023
Subjects:
Q Science (General)
Online Access:http://eprints.utm.my/105275/
http://dx.doi.org/10.1007/s10462-022-10202-8
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spelling my.utm.1052752024-04-24T06:06:05Z http://eprints.utm.my/105275/ Diabetes subtypes classification for personalized health care: A review Omar, Nashuha Nazirun, Nisha Nadhira Vijayam, Bhuwaneswaran Abdul Wahab, Asnida Ahmad Bahuri, Hana Q Science (General) Healthcare is evolving from standard to personalized, driven by the patients’ needs. Personalized healthcare is a medical model based on genetics, genomics, and other biological information that helps to predict risk for disease. To date, machine learning and data mining are the fastest-growing healthcare field used to classify patient cohorts from a large dataset and its application for diabetes subtyping will be a breakthrough. In this review paper, we have identified, analyzed, and summarized how previous studies distinguished diabetes into subtypes besides implementing the methods for diabetes subtyping using data mining and various clustering algorithms. We have discovered that many studies have suggested diabetes can be differentiated into subtypes clinically based on the risk complications, genetically defined, using clinical features, and for treatment selection. As for clustering algorithms, k-means clustering and hierarchical clustering were shown to be widely used in determining sub-clusters of diabetes. To further investigate diabetes subtyping, understanding the specific objective and method of diabetes subtyping using clustering algorithms from a large dataset will be crucial which could contribute to novel knowledge and improvement for diabetes management. Springer Nature 2023 Article PeerReviewed Omar, Nashuha and Nazirun, Nisha Nadhira and Vijayam, Bhuwaneswaran and Abdul Wahab, Asnida and Ahmad Bahuri, Hana (2023) Diabetes subtypes classification for personalized health care: A review. Artificial Intelligence Review, 56 (3). pp. 2697-2721. ISSN 0269-2821 http://dx.doi.org/10.1007/s10462-022-10202-8 DOI : 10.1007/s10462-022-10202-8
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic Q Science (General)
spellingShingle Q Science (General)
Omar, Nashuha
Nazirun, Nisha Nadhira
Vijayam, Bhuwaneswaran
Abdul Wahab, Asnida
Ahmad Bahuri, Hana
Diabetes subtypes classification for personalized health care: A review
description Healthcare is evolving from standard to personalized, driven by the patients’ needs. Personalized healthcare is a medical model based on genetics, genomics, and other biological information that helps to predict risk for disease. To date, machine learning and data mining are the fastest-growing healthcare field used to classify patient cohorts from a large dataset and its application for diabetes subtyping will be a breakthrough. In this review paper, we have identified, analyzed, and summarized how previous studies distinguished diabetes into subtypes besides implementing the methods for diabetes subtyping using data mining and various clustering algorithms. We have discovered that many studies have suggested diabetes can be differentiated into subtypes clinically based on the risk complications, genetically defined, using clinical features, and for treatment selection. As for clustering algorithms, k-means clustering and hierarchical clustering were shown to be widely used in determining sub-clusters of diabetes. To further investigate diabetes subtyping, understanding the specific objective and method of diabetes subtyping using clustering algorithms from a large dataset will be crucial which could contribute to novel knowledge and improvement for diabetes management.
format Article
author Omar, Nashuha
Nazirun, Nisha Nadhira
Vijayam, Bhuwaneswaran
Abdul Wahab, Asnida
Ahmad Bahuri, Hana
author_facet Omar, Nashuha
Nazirun, Nisha Nadhira
Vijayam, Bhuwaneswaran
Abdul Wahab, Asnida
Ahmad Bahuri, Hana
author_sort Omar, Nashuha
title Diabetes subtypes classification for personalized health care: A review
title_short Diabetes subtypes classification for personalized health care: A review
title_full Diabetes subtypes classification for personalized health care: A review
title_fullStr Diabetes subtypes classification for personalized health care: A review
title_full_unstemmed Diabetes subtypes classification for personalized health care: A review
title_sort diabetes subtypes classification for personalized health care: a review
publisher Springer Nature
publishDate 2023
url http://eprints.utm.my/105275/
http://dx.doi.org/10.1007/s10462-022-10202-8
_version_ 1797905986749464576
score 13.211869

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