Disease risk level prediction based on knowledge driven optimized deep ensemble framework

Abstract

Healthcare information in diverse formats has been steadily boosting the growth of health systems. This information covers a wide range of new sources, such as computer files, cell phones, and health monitoring gadgets. Big health data expands the additional possibilities for analyzing the health data that improve healthcare services. Deep learning can be used in collaboration with information fusion approaches to generate better comprehensive and trustworthy predictions from large healthcare information. In this study, a risk level prediction framework is established with four main stages: pre-processing, knowledge extraction, feature extraction, as well as classification and prediction. The obtained data from the Electronic Health Records (EHR) is first exposed to a pre-processing stage, where the data cleaning and tokenization operations are performed. Then, the knowledge extraction takes place via ontology-based knowledge extraction and improved semantic similarity. In addition, the statistical and information entropy features are retrieved. To forecast the health risks from HER, a novel Ensemble Classifier (EC) with Neural network (NN), optimized Deep Belief Network (DBN) & Fuzzy logic is introduced. The extracted features are utilized to train the NN, and the retrieved knowledge bases are utilized to train the fuzzy logic. The outcome from NN as well as fuzzy logic is given as a source of input to the DBN, where the final risk prediction of the disease takes place. Since DBN is the key indicator; its weight is fine-tuned utilizing a new hybrid approach known as Rain Leveraged Dynamic Butterfly Optimization (RLDBO) to improve risk level prediction accuracy.