1- Department of Health Service Administration, ST.C., Islamic Azad University, Tehran, Iran
2- Department of Health Service Administration, ST.C., Islamic Azad University, Tehran, Iran ,Shessam@iau.ac.ir
3- 1. Department of Future Studies and Theory Building, Iranian Academy of Medical Sciences, Tehran, Iran 2. National Center for Health Insurance Research, Tehran, Iran
2- Department of Health Service Administration, ST.C., Islamic Azad University, Tehran, Iran ,
3- 1. Department of Future Studies and Theory Building, Iranian Academy of Medical Sciences, Tehran, Iran 2. National Center for Health Insurance Research, Tehran, Iran
Abstract: (20 Views)
Background: Diabetes is a metabolic disorder in the body. Using data mining techniques is useful for predicting diabetes, so the aim of this study was to predict diabetes status using artificial neural network and decision tree models.
Methods: This study was descriptive and based on secondary data. Data from 4820 individuals were also analyzed. In this study, the performance of two decision tree models and artificial neural networks was compared. Data was randomly divided into three parts, 70% as training, 20% as validation, and 10% as testing. Various criteria such as accuracy, precision, specificity, sensitivity, ROC-AUC curve, and F1-Score were used to evaluate the models, and finally, the best algorithm for predicting diabetes was identified.
Results: The decision tree and artificial neural network models were obtained with 97% Precision and 97% accuracy and 96% Precision and 96% accuracy, respectively. The area under the ROC curve in the artificial neural network model (95%) was higher in the training and testing sets than the decision tree model (92%).
Conclusion: Although the accuracy of the decision tree model in predicting diabetes status was slightly higher than that of the artificial neural network model, the area under the curve (AUC) of the neural network was higher, and therefore, both models performed well. According to these two models, the variables of fasting blood sugar, systolic blood pressure, and age were effective variables in predicting diabetes status.
Methods: This study was descriptive and based on secondary data. Data from 4820 individuals were also analyzed. In this study, the performance of two decision tree models and artificial neural networks was compared. Data was randomly divided into three parts, 70% as training, 20% as validation, and 10% as testing. Various criteria such as accuracy, precision, specificity, sensitivity, ROC-AUC curve, and F1-Score were used to evaluate the models, and finally, the best algorithm for predicting diabetes was identified.
Results: The decision tree and artificial neural network models were obtained with 97% Precision and 97% accuracy and 96% Precision and 96% accuracy, respectively. The area under the ROC curve in the artificial neural network model (95%) was higher in the training and testing sets than the decision tree model (92%).
Conclusion: Although the accuracy of the decision tree model in predicting diabetes status was slightly higher than that of the artificial neural network model, the area under the curve (AUC) of the neural network was higher, and therefore, both models performed well. According to these two models, the variables of fasting blood sugar, systolic blood pressure, and age were effective variables in predicting diabetes status.
Review: Applicable |
Subject:
Health information management
Received: 2026/03/1 | Accepted: 2026/05/5
Received: 2026/03/1 | Accepted: 2026/05/5
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