Prediction of Type 2 Diabetes Based on Machine Learning Algorithm

Prediction of type 2 diabetes (T2D) occurrence allows a person at risk to take actions that can prevent onset or delay the progression of the disease. In this study, we developed a machine learning (ML) model to predict T2D occurrence in the following year (Y + 1) using variables in the current year...

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Bibliographic Details
Published in:International journal of environmental research and public health Vol. 18; no. 6; p. 3317
Main Authors: Deberneh, Henock M., Kim, Intaek
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 23.03.2021
MDPI
Subjects:
Algorithms
Artificial intelligence
Asthma
Datasets
Diabetes
Diabetes Mellitus, Type 2 - epidemiology
Discriminant analysis
Disease
Electronic health records
Feature selection
Humans
Insulin
Logistic Models
Machine Learning
Medical records
Metabolism
Neural networks
Pediatrics
Prediabetic State
Quality of life
Statistical analysis
Support vector machines
Variance analysis
prediction
machine learning
type 2 diabetes
ISSN:1660-4601, 1661-7827, 1660-4601
Online Access:Get full text
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Summary:Prediction of type 2 diabetes (T2D) occurrence allows a person at risk to take actions that can prevent onset or delay the progression of the disease. In this study, we developed a machine learning (ML) model to predict T2D occurrence in the following year (Y + 1) using variables in the current year (Y). The dataset for this study was collected at a private medical institute as electronic health records from 2013 to 2018. To construct the prediction model, key features were first selected using ANOVA tests, chi-squared tests, and recursive feature elimination methods. The resultant features were fasting plasma glucose (FPG), HbA1c, triglycerides, BMI, gamma-GTP, age, uric acid, sex, smoking, drinking, physical activity, and family history. We then employed logistic regression, random forest, support vector machine, XGBoost, and ensemble machine learning algorithms based on these variables to predict the outcome as normal (non-diabetic), prediabetes, or diabetes. Based on the experimental results, the performance of the prediction model proved to be reasonably good at forecasting the occurrence of T2D in the Korean population. The model can provide clinicians and patients with valuable predictive information on the likelihood of developing T2D. The cross-validation (CV) results showed that the ensemble models had a superior performance to that of the single models. The CV performance of the prediction models was improved by incorporating more medical history from the dataset.
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ISSN:1660-4601
1661-7827
1660-4601
DOI:10.3390/ijerph18063317