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A Multiclass Approach to Predicting Diabetes Stage Using Machine Learning
Emmanuel Mbuya
,
Tsholofelo Diphoko Mokheleli
, Tebogo Bokaba
, Patrick Ndayizigamiye
Applied Information Systems
University of Johannesburg
Research output
:
Contribution to conference
›
Paper
›
peer-review
1
Citation (Scopus)
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Keyphrases
Machine Learning
100%
Diabetes
100%
Multi-class
100%
Dimensionality Reduction
66%
Machine Learning Algorithms
66%
Public Health Risk
33%
Disease Prevention
33%
Global Prevalence
33%
Predictive Models
33%
Deep Learning Methods
33%
Precision-recall
33%
Centers for Disease Control
33%
Logistic Regression Model
33%
Area under the Receiver Operating Characteristic Curve
33%
Balanced Accuracy
33%
F1 Score
33%
Social Determinants of Health (SDoH)
33%
Prevalence of Diabetes Mellitus
33%
XGBoost Model
33%
Medical Indicator
33%
Diabetes Risk
33%
Computer Science
Machine Learning Algorithm
100%
Dimensionality Reduction
100%
Machine Learning
100%
Learning System
100%
Extreme Gradient Boosting
50%
Deep Learning Technique
50%
Large Data Set
50%
Centers for Disease Control
50%
Predictive Model
50%
Logistic Regression
50%
Social Sciences
Learning Method
100%
Social Determinant
100%
Disease Control
100%
Preventive Medicine
100%
Nutrition Policy
100%
Engineering
Machine Learning Algorithm
100%
Learning System
100%
Dimensionality
50%
Learning Technique
50%
Metrics
50%
Receiver Operating Characteristic
50%
Deep Learning Method
50%
Dimensionality Reduction Technique
50%
Mathematics
Dimensionality Reduction
100%
Logistic Regression
50%
Predictive Model
50%
Reduction Method
50%
Deep Learning Method
50%
Chemical Engineering
Learning System
100%
Deep Learning Method
33%