Real-Data Comparison of Data Mining Methods in Prediction of Diabetes in Iran / 대한의료정보학회지
Healthcare Informatics Research
;
: 177-185, 2013.
Article
in English
| WPRIM
| ID: wpr-167420
ABSTRACT
OBJECTIVES:
Diabetes is one of the most common non-communicable diseases in developing countries. Early screening and diagnosis play an important role in effective prevention strategies. This study compared two traditional classification methods (logistic regression and Fisher linear discriminant analysis) and four machine-learning classifiers (neural networks, support vector machines, fuzzy c-mean, and random forests) to classify persons with and without diabetes.METHODS:
The data set used in this study included 6,500 subjects from the Iranian national non-communicable diseases risk factors surveillance obtained through a cross-sectional survey. The obtained sample was based on cluster sampling of the Iran population which was conducted in 2005-2009 to assess the prevalence of major non-communicable disease risk factors. Ten risk factors that are commonly associated with diabetes were selected to compare the performance of six classifiers in terms of sensitivity, specificity, total accuracy, and area under the receiver operating characteristic (ROC) curve criteria.RESULTS:
Support vector machines showed the highest total accuracy (0.986) as well as area under the ROC (0.979). Also, this method showed high specificity (1.000) and sensitivity (0.820). All other methods produced total accuracy of more than 85%, but for all methods, the sensitivity values were very low (less than 0.350).CONCLUSIONS:
The results of this study indicate that, in terms of sensitivity, specificity, and overall classification accuracy, the support vector machine model ranks first among all the classifiers tested in the prediction of diabetes. Therefore, this approach is a promising classifier for predicting diabetes, and it should be further investigated for the prediction of other diseases.
Full text:
Available
Index:
WPRIM (Western Pacific)
Main subject:
Logistic Models
/
Mass Screening
/
Prevalence
/
Cross-Sectional Studies
/
Risk Factors
/
ROC Curve
/
Sensitivity and Specificity
/
Developing Countries
/
Data Mining
/
Support Vector Machine
Type of study:
Diagnostic study
/
Etiology study
/
Observational study
/
Prevalence study
/
Prognostic study
/
Risk factors
/
Screening study
Limits:
Humans
Country/Region as subject:
Asia
Language:
English
Journal:
Healthcare Informatics Research
Year:
2013
Type:
Article
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