Volume 8, Issue 2 (Summer 2022)
JMIS 2022, 8(2): 126-139 |
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Hosseinpoor M. Predicting Gestational Diabetes Using an Intelligent Algorithm Based on Artificial Neural Network. JMIS 2022; 8 (2) :126-139
URL: http://jmis.hums.ac.ir/article-1-335-en.html
URL: http://jmis.hums.ac.ir/article-1-335-en.html
Department of Computer Engineering, Estahban Branch, Islamic Azad University, Estahban, Iran.
Abstract: (1248 Views)
Objective Due to the large amount of data for people with diabetes, it is very difficult to extract the predictors of diabetes. Data mining science can discover the predictors of diseases and help physicians and medical staff in predicting and diagnosing diseases.
Methods This is an applied survey study conducted in 2020 using the dataset used by Mirsharif et al. The study population includes 105 cases with data registered from 2011 to 2014 in a specialized women’s medical center in Tehran, of which 80 were for healthy women and 25 were for women with gestational diabetes. MATLAB software was used to analyze and evaluate the results.
Results The results and comparisons showed the high efficiency of the proposed method in predicting gestational diabetes. The accuracy of the proposed method was 93%, which was more accurate than the method proposed by Mirsharif et al.
Conclusion The proposed prediction method has good performance and high accuracy compared to previous methods. Therefore, this intelligent and unsupervised method can be used to predict gestational diabetes.
Methods This is an applied survey study conducted in 2020 using the dataset used by Mirsharif et al. The study population includes 105 cases with data registered from 2011 to 2014 in a specialized women’s medical center in Tehran, of which 80 were for healthy women and 25 were for women with gestational diabetes. MATLAB software was used to analyze and evaluate the results.
Results The results and comparisons showed the high efficiency of the proposed method in predicting gestational diabetes. The accuracy of the proposed method was 93%, which was more accurate than the method proposed by Mirsharif et al.
Conclusion The proposed prediction method has good performance and high accuracy compared to previous methods. Therefore, this intelligent and unsupervised method can be used to predict gestational diabetes.
Type of Study: Research |
Subject:
Special
Received: 2021/08/16 | Accepted: 2022/04/11 | Published: 2022/07/1
Received: 2021/08/16 | Accepted: 2022/04/11 | Published: 2022/07/1
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