A hybrid Decision Support System combining multiple classification models predicted the risk of retinopathy development in Type 1 Diabetes Mellitus patients with an accuracy of 98%.
The aim of the present study is to design and develop a Decision Support System (DSS) closely coupled with an Electronic Medical Record (EMR), able to predict the risk of a Type 1 Diabetes Mellitus (T1DM) patient to develop retinopathy. The proposed system is able to store a wealth of information regarding the clinical state of the T1DM patient and continuously provide the health experts with predictions regarding the possible future complications that he may present. The DSS is a hybrid infrastructure combining a Feedforward Neural Network (FNN), a Classification and Regression Tree (CART) and a Rule Induction C5.0 classifier, with an improved Hybrid Wavelet Neural Network (iHWNN). A voting mechanism is utilized to merge the results from the four classification models. The proposed DSS has been trained and evaluated using data from 55 T1DM patients, acquired by the Athens Hippokration Hospital in close collaboration with the EURODIAB research team. The DSS has shown an excellent performance resulting in an accuracy of 98%. Care has been taken to design and implement a consistent and continuously evolving Information Technology (IT) system by utilizing technologies such as smart agents periodically triggered to retrain the DSS with new cases added in the data repository.
Skevofilakas et al. (Sun,) conducted a other in Type 1 Diabetes Mellitus (T1DM) retinopathy (n=55). Hybrid Decision Support System (DSS) was evaluated on Prediction accuracy for retinopathy development. A hybrid Decision Support System combining multiple classification models predicted the risk of retinopathy development in Type 1 Diabetes Mellitus patients with an accuracy of 98%.