By Research Fellow Nagesh Shukla
Diabetes is spreading all around the world as an unprecedented epidemic. According to a report from International Diabetes Federation, in 2011, 366 million people had diabetes and by 2030 this will have risen to 552 million (8.3% compared to 9.9% of the adult population, respectively).
Among the many related issues of diabetes management, its complications constitute the main part of the heavy burden of this disease. The aim of this study was to develop a risk prediction model to predict the chances of diabetes complications based on varying risk factors. The methodology proposed in this study is presented in Fig. 1. We started with a list of diabetes complications and their correlated predisposing factors are derived from the existing endocrinology literature. A data meta-analysis method has been employed to extract and combine the numeric value of the relationships between these two. Then, data analysis method based on regression models and artificial neural networks (ANN) were used to model the correspondence between factors and complications (one to one model). These (one to one) models related to an individual complication were integrated using the naïve Bayes theorem. Finally, a Bayesian belief network was developed to show the influence of all risk factors and complications on each other. We assessed the predictive power of these models by R^2, F-ratio and adjusted R^2 equations. Also, sensitivity, specificity and positive predictive value were calculated to evaluate the final model using real patient data. The results suggest that the best fitted regression models outperform the predictive ability of an ANN model, as well as six other regression patterns for all one to one models.
Figure 1: A perspective of system development steps
For detailed information about this study:
Mohsen Sangi, Khin Than Win, Farid Shirvani, Mohammad-Reza Namazi-Rad, Nagesh Shukla, (2015) “Applying a Novel Combination of Techniques to Develop a Predictive Model for Diabetes Complications” Plos One. Published: April 22, 2015 DOI: 10.1371/journal.pone.0121569