An Efficient Disease Prediction System with Electronic Health Record Using Feature Selection and Convolutional Recurrent Neural Network

Abstract

Rapid advancements in communication technologies and expert systems generate massive amounts of medical data. Using an electronic health record (EHR) and a disease prediction system, we can predict high-risk patients at an early stage. The use of EHR for early prediction aids in the improvement of health care quality through personalized medicine. To analyze the massive amount of disease-related EHR data, various deep learning methods for disease prediction are proposed. Deep learning is a machine learning technique that has evolved. Even with more deep learning techniques in place, no healthcare system has achieved a higher level of disease prediction accuracy. In this paper, we propose a disease prediction system for serious, lethal diseases such as heart, diabetes, and cancer diseases. The proposed system is made up of a feature selection algorithm (GBCOA) and a Convolutional-Recurrent Neural Network (C-RNN). This proposed model was tested against existing methods and found to have higher prediction accuracy with less computation time.