Healthc Inform Res.  2013 Sep;19(3):177-185. 10.4258/hir.2013.19.3.177.

Real-Data Comparison of Data Mining Methods in Prediction of Diabetes in Iran

• Tapak L
• Mahjub H
• Hamidi O
• Poorolajal J

• KMID: 2166696
• DOI: http://doi.org/10.4258/hir.2013.19.3.177

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.

Keyword

Diabetes; Cluster Sampling; Data Mining; Support Vector Machine; Logistic Regression

MeSH Terms

Cross-Sectional Studies
Data Mining
Developing Countries
Humans
Iran
Logistic Models
Mass Screening
Prevalence
Risk Factors
ROC Curve
Sensitivity and Specificity
Support Vector Machine

Figure

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  Figure 1 Performance criteria of the six classification methods.PPV: positive predictive value, NPV: negative predictive value, AUC; area under ROC curve, ROC: receiver operating characteristic.

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  Figure 2 Receiver operating characteristic (ROC) curves for comparison of the six classification methods: (A) linear discriminant analysis, (B) logistic regression, (C) fuzzy c-mean, (D) support vector machine, (E) neural network, and (F) random forest. AUC: area under ROC curve.

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