Skip to main navigation
Skip to search
Skip to main content
UTMB Health Research Expert Profiles Home
Help & FAQ
Link opens in a new tab
Search content at UTMB Health Research Expert Profiles
Home
Experts
Departments
Equipment
Projects/Grants
Publications
Activities
Press/Media
Honors
Impacts
Prediction of type 2 diabetes based on machine learning algorithm
Henock M. Deberneh
, Intaek Kim
Research output
:
Contribution to journal
›
Article
›
peer-review
232
Link opens in a new tab
Scopus citations
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Prediction of type 2 diabetes based on machine learning algorithm'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
Type 2 Diabetes Mellitus (T2DM)
100%
Prediction Model
100%
Type 2 Diabetes Prediction
100%
Machine Learning Algorithms
100%
Diabetes
33%
Support Vector Machine
33%
Physical Activity
33%
Medical History
33%
Triglycerides
33%
Disease Progression
33%
Non-diabetic
33%
Logistic Regression
33%
Hemoglobin A1c (HbA1c)
33%
Chi-square Test
33%
Uric Acid
33%
Family History
33%
Superior Performance
33%
Single Model
33%
Elimination Method
33%
Ensemble Model
33%
Predictive Information
33%
Individuals at Risk
33%
Pre-diabetes (pre-DM)
33%
Electronic Health Records
33%
ANOVA Test
33%
Results Validation
33%
Korean Population
33%
Machine Learning Models
33%
Activity History
33%
Recursive Feature Elimination
33%
Regression Forest
33%
Ensemble Machine Learning
33%
γ-GTP
33%
Fasting Blood Glucose
33%
Algorithm-based
33%
Medical Institutes
33%
XGBoost Machine Learning Model
33%
Medicine and Dentistry
Maturity Onset Diabetes of the Young
100%
Machine Learning Algorithm
100%
Prediction Model
60%
Cross-Validation
40%
Diabetes
20%
Diabetes Mellitus
20%
Clinician
20%
Physical Activity
20%
Chi Square Test
20%
Body Mass Index
20%
Logistic Regression Analysis
20%
Triacylglycerol
20%
Impaired Glucose Tolerance
20%
Analysis of Variance
20%
Uric Acid
20%
Family History
20%
Guanosine Triphosphate
20%
Hemoglobin A1c
20%
Electronic Health Record
20%
Diseases
20%
Xgboost
20%
Medical History
20%
Glucose
20%
Computer Science
Prediction Model
100%
Machine Learning Algorithm
100%
Cross-Validation
66%
Experimental Result
33%
Random Decision Forest
33%
Logistic Regression
33%
Analysis of Variance
33%
Extreme Gradient Boosting
33%
Elimination Method
33%
Superior Performance
33%
Machine Learning Model
33%
Support Vector Machine
33%
Mathematics
Cross-Validation
100%
Logistic Regression
50%
Analysis of Variance
50%
Support Vector Machine
50%
Ensemble Model
50%
Chi Square Test
50%
Xgboost
50%
Likelihood
50%