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

Healthcare Informatics Research

Ali Garavand¹  Hajar Hasannejadasl²  Sharareh R. Niakan Kalhori²  Shahabeddin Abhari²  Mehdi Ebrahimi³ 

[1] Department of Health Information Management and Technology, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, .Iran;Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, .Iran;Department of Internal Medicine, School of Medicine, Tehran University of Medical Sciences, Tehran, .Iran;

关键词: artificial intelligence;    diabetes mellitus;    machine learning;    diabetes care;    health informatics;   

DOI  :  10.4258/hir.2019.25.4.248

来源: DOAJ

【 摘 要 】

ObjectivesThe 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.MethodsThis 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.ResultsThe 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.ConclusionsIt 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.

【 授权许可】

Unknown