Healthc Inform Res.  2016 Apr;22(2):95-100. 10.4258/hir.2016.22.2.95.

Comparison of Predictive Models for the Early Diagnosis of Diabetes

• Jahani M
• Mahdavi M

• KMID: 2166941
• DOI: http://doi.org/10.4258/hir.2016.22.2.95

Abstract

OBJECTIVES
This study develops neural network models to improve the prediction of diabetes using clinical and lifestyle characteristics. Prediction models were developed using a combination of approaches and concepts.
METHODS
We used memetic algorithms to update weights and to improve prediction accuracy of models. In the first step, the optimum amount for neural network parameters such as momentum rate, transfer function, and error function were obtained through trial and error and based on the results of previous studies. In the second step, optimum parameters were applied to memetic algorithms in order to improve the accuracy of prediction. This preliminary analysis showed that the accuracy of neural networks is 88%. In the third step, the accuracy of neural network models was improved using a memetic algorithm and resulted model was compared with a logistic regression model using a confusion matrix and receiver operating characteristic curve (ROC).
RESULTS
The memetic algorithm improved the accuracy from 88.0% to 93.2%. We also found that memetic algorithm had a higher accuracy than the model from the genetic algorithm and a regression model. Among models, the regression model has the least accuracy. For the memetic algorithm model the amount of sensitivity, specificity, positive predictive value, negative predictive value, and ROC are 96.2, 95.3, 93.8, 92.4, and 0.958 respectively.
CONCLUSIONS
The results of this study provide a basis to design a Decision Support System for risk management and planning of care for individuals at risk of diabetes.

Keyword

Diabetes; Decision Support Techniques; Neural Networks; Early Diagnosis

MeSH Terms

Decision Support Techniques
Early Diagnosis*
Life Style
Logistic Models
Neural Networks (Computer)
Risk Management
ROC Curve
Sensitivity and Specificity
Weights and Measures

Figure

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  Figure 1 Steps to develop predictive models.

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  Figure 2 Encoding of chromosome.

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  Figure 3 Implementation process of memetic algorithm.

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  Figure 4 Analysis of model using receiver operating characteristic curve (ROC). GA: genetic algorithm model, LR: logistic regression model.

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