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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2016.1
- 수록면
- 95 - 100 (6page)
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초록· 키워드
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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.
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참고문헌 (23)
참고문헌 신청
International Diabetes Federation / 2013 / IDF diabetes atlas
Centers for Disease Control and Prevention / National Diabetes Fact Sheet: national estimates and general information on diabetes and prediabetes in the United States, 2011
Pei E / 2014 / Effects of lipids and lipoproteins on diabetic foot in people with type 2 diabetes mellitus: a meta-analysis / J Diabetes Complicat 28(4):559~564
Zaugg SD / 2014 / Diabetes numeracy and blood glucose control: association with type of diabetes and source of care / Clin Diabetes 32(4):152~157
Nielsen DS / 2014 / Beyond genetics: Influence of dietary factors and gut microbiota on type 1 diabetes / FEBS Lett 588(22):4234~4243
Centers for Disease Control and Prevention / National Diabetes Fact Sheet: national estimates and general information on diabetes and prediabetes in the United States, 2011
Pei E / 2014 / Effects of lipids and lipoproteins on diabetic foot in people with type 2 diabetes mellitus: a meta-analysis / J Diabetes Complicat 28(4):559~564
Zaugg SD / 2014 / Diabetes numeracy and blood glucose control: association with type of diabetes and source of care / Clin Diabetes 32(4):152~157
Nielsen DS / 2014 / Beyond genetics: Influence of dietary factors and gut microbiota on type 1 diabetes / FEBS Lett 588(22):4234~4243
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