Journal of Modern Medical

Introduction
Diagnosing diseases is a very challenging and complicated process for experts in medical sciences; therefore, it is necessary to use appropriate data mining methods to make a correct diagnosis in medical issues. The use of algorithms and data mining techniques can provide patterns and data regarding the factors of various diseases; based on these data, doctors and medical professionals can act for the prevention of diseases. Due to the existence of a huge amount of data about patients with diabetes, it is not be possible to extract predictors of diabetes from this huge amount of data. Data mining science can discover the predictors of diseases and help doctors and medical staff in the prediction and diagnosis of diseases.
Methods
The is an applied survey study conducted in 2020 using the dataset of Mirsharif et al.’s study. The study population includes 105 female patient data registered from 2011 to 2014 in a specialized women’s medical clinic in Tehran, of which 80 were for healthy women and 25 were for women with gestational diabetes. MATLAB software was used to analyze and check the results. The used algorithm was a hybrid artificial neural network- genetic algorithm. First, unsupervised clustering operation was performed to create two clusters of diabetic and non-diabetic women using genetic algorithm on the input dataset. After performing 100 times of execution, the best extract containing the optimal clusters was obtained. After this step, the output of the genetic algorithm was considered as the input of the artificial neural network to perform the training operation. The neural network was a feedforward neural network with 20 hidden layers. In this study, by default, 75% of the datasets were used as learning samples for system learning. Moreover, to check the accuracy of the system, 25% of the datasets were used for test and validation.
Results
The results and comparisons showed the high efficiency of the proposed method in predicting gestational diabetes. In the test dataset, 25 samples were used to evaluate the method, among which 15 were for non-diabetic women and 10 were for diabetic women. In the test of the proposed method, according to the produced confusion matrix, all 12 predictions of non-diabetic women were correctly classified. Out of 13 predictions of diabetic women, 69.2% were correctly classified and 30.8% were incorrectly classified. Out of 16 samples of non-diabetic women, 75% were correctly predicted as healthy women, while 25% were diabetic. All 9 samples of diabetic women were correctly classified as diabetic. Overall, 84% of predictions were correct and 16% were incorrect (Figure 1). The performance of the proposed method was compared with the method proposed by Mirsharif et al. on the same dataset based on two criteria. As can be seen in Table 1, the efficiency and performance of the proposed method was better than the Mirsharif et al.’s method in both MSE and ACC criteria.


This indicates that the proposed method in unsupervised mode had the desired efficiency in facing different datasets to accurately diagnose gestational diabetes.
Discussion
In this study, an intelligent and unsupervised approach based on neural-artificial network techniques and genetic algorithm was used to predict gestational diabetes. The proposed method seeks to improve the process of differentiating women with gestational diabetes from healthy women. The neural network in the proposed method discovers hidden patterns in diabetic women compared to healthy women by examining the desired dataset. Since the function of the neural network depends on how its structure is defined, a genetic algorithm was used along with the proposed neural network whose task was to learn the structure of the neural network without supervision. The results of the implementation of the proposed system showed that the performance and accuracy of the algorithm in the desired dataset, compared to the previous similar system, were acceptable and improved significantly by 93.2%. Therefore, this intelligent and unsupervised method can be used to predict gestational diabetes.

Ethical Considerations
Compliance with ethical guidelines
This study has ethical approval from Estahban Branch, Islamic Azad University (Code: 1144819916004881397187659556 ).

Funding
This research did not receive any grant from funding agencies in the public, commercial, or non-profit sectors. 

Conflicts of interest
The authors declared no conflict of interest.

Acknowledgements
The author would like to thank Estahban Branch, Islamic Azad University for their cooperation.
 

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