INTELLIGENT DEEP LEARNING BASED PREDICTIVE MODEL FOR CORONARY HEART DISEASE AND CHRONIC KIDNEY DISEASE ON PEOPLE WITH DIABETES MELLITUS - Mohamed , A. Thasil; Santhoshkumar , Sundar; Varadarajan, Vijayakumar (Malaysian Journal of Computer Science, 2022)
Presently, process analytics extracts the knowledge from the past data to explore, monitor, and improve the processes. The recently developed deep learning (DL) models find it helpful to analyse medical data and make decisions. Among various diseases, type 2 diabetes mellitus (T2DM) becomes a widespread disease over the globe and it leads to severe outcomes. Chronic kidney disease (CKD) and coronary heart disease (CHD) are the major illness occurred in people with T2DM. Since the earlier prediction of the risk factors related to CKD and CHD on T2DM persons is necessary, this study focuses on the design of intelligent feature selection with deep learning based risk factor prediction (IFS-DLRFP) model. The proposed IFS-DLRFP technique intends to determine the early warning to the patients with T2DM to develop CKD or CHD. In addition, the IFS-DLRFP technique includes the design of fruit fly optimization algorithm (FFOA) based feature selection technique to choose an optimal set of features. Moreover, firefly optimization with gated recurrent unit (FF-GRU) based classification technique is derived to allocate appropriate class labels to the input data. The FF-GRU technique performs the hyperparameter tuning process using FF technique. In order to ensure the better performance of the IFS-DLRFP technique, a wide range of simulations take place on benchmark datasets and the simulation outcomes reported the supremacy of the IFS-DLRFP approach over the recent techniques.