Artificial Intelligence Applications in Type 2 Diabetes Mellitus Care: Focus on Machine Learning Methods

Shahabeddin Abhari; Sharareh R. Niakan Kalhori; Mehdi Ebrahimi; Hajar Hasannejadasl; Ali Garavand

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자료유형: 학술저널

저자정보: Shahabeddin Abhari Sharareh R. Niakan Kalhori Mehdi Ebrahimi Hajar Hasannejadasl Ali Garavand

저널정보: 대한의료정보학회 Healthcare Informatics Research Healthcare Informatics Research 제25권 제4호

발행연도: 2019.1

수록면: 248 - 261 (14page)

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Objectives: The incidence of type 2 diabetes mellitus has increased significantly in recent years. With the development of artificial intelligence applications in healthcare, they are used for diagnosis, therapeutic decision making, and outcome prediction, especially in type 2 diabetes mellitus. This study aimed to identify the artificial intelligence (AI) applications for type 2 diabetes mellitus care. Methods: This is a review conducted in 2018. We searched the PubMed, Web of Science, and Embase scientific databases, based on a combination of related mesh terms. The article selection process was based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Finally, 31 articles were selected after inclusion and exclusion criteria were applied. Data gathering was done by using a data extraction form. Data were summarized and reported based on the study objectives. Results: The main applications of AI for type 2 diabetes mellitus care were screening and diagnosis in different stages. Among all of the reviewed AI methods, machine learning methods with 71% (n = 22) were the most commonly applied techniques. Many applications were in multi method forms (23%). Among the machine learning algorithms applications, support vector machine (21%) and naive Bayesian (19%) were the most commonly used methods. The most important variables that were used in the selected studies were body mass index, fasting blood sugar, blood pressure, HbA1c, triglycerides, low-density lipoprotein, high-density lipoprotein, and demographic variables. Conclusions: It is recommended to select optimal algorithms by testing various techniques. Support vector machine and naive Bayesian might achieve better performance than other applications due to the type of variables and targets in diabetes-related outcomes classification.

참고문헌 (75)

참고문헌 신청

Danaei G / 2011 / National, regional, and global trends in fasting plasma glucose and diabetes prevalence since 1980: systematic analysis of health examination surveys and epidemiological studies with 370 country-years and 2.7 million participants / Lancet 378(9785):31~40

World Health Organization / 2010 / Global status report on noncommunicable diseases 2010

World Health Organization / 1999 / Definition, diagnosis and classification of diabetes mellitus and its complications:report of a WHO consultation. Part 1: Diagnosis and classification of diabetes mellitus

American Diabetes Association / 2014 / Diagnosis and classification of diabetes mellitus / Diabetes Care 37(Supplement 1):S81~S90

World Health Organization / 2016 / Global report on diabetes

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