Healthc Inform Res.  2019 Oct;25(4):248-261. 10.4258/hir.2019.25.4.248.

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

• Abhari S
• Niakan Kalhori SR
• Ebrahimi M
• Hasannejadasl H
• Garavand A

• KMID: 2462223
• DOI: http://doi.org/10.4258/hir.2019.25.4.248

Abstract

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.

Keyword

Artificial Intelligence; Diabetes Mellitus; Machine Learning; Diabetes Care; Health Informatics

MeSH Terms

Artificial Intelligence*
Blood Glucose
Blood Pressure
Body Mass Index
Classification
Decision Making
Delivery of Health Care
Diabetes Mellitus
Diabetes Mellitus, Type 2*
Diagnosis
Fasting
Incidence
Lipoproteins
Machine Learning*
Mass Screening
Methods*
Support Vector Machine
Triglycerides
Blood Glucose
Lipoproteins
Triglycerides

Figure

• 

  Figure 1 Process of PRISMA for data collection.

• 

  Figure 2 Frequency (percentage) of artificial intelligence methods used in type 2 diabetes mellitus. ML: machine learning, FL: fuzzy logic, ES: expert system, KB: knowledge base, NLP: natural language processing.

• 

  Figure 3 Frequency of machine learning algorithms used for type 2 diabetes mellitus care. SVM: support vector machine, ANN: artificial neural network, NB: naïve Bayes, DT: decision tree, RF: random forest, CART: classification and regression trees, KNN: k-nearest neighbor.

• 

  Figure 4 Frequency of artificial intelligence applications for health aspects of type 2 diabetes mellitus.

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