first_pagesettingsOrder Article Reprints Open AccessArticle Deep Q-Network (DQN) Model for Disease Prediction Using Electronic Health Records (EHRs) by Nabil M. AbdelAziz 1ORCID,Gehan A. Fouad 2,Safa Al-Saeed 1,* andAmira M. Fawzy 1ORCID 1 Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, Sharqiyah, Egypt 2 Department of Data Science, Faculty of Artificial Intelligence, Egyptian Russian University, Badr City 11829, Cairo, Egypt * Author to whom correspondence should be addressed. Sci 2025, 7(1), 14; https://doi.org/10.3390/sci7010014 Submission received: 16 October 2024 / Revised: 19 December 2024 / Accepted: 16 January 2025 / Published: 7 February 2025 (This article belongs to the Section Computer Sciences, Mathematics and AI) Downloadkeyboard_arrow_down Browse Figures Versions Notes Abstract Many efforts have proved that deep learning models are effective for disease prediction using electronic health records (EHRs). However, these models are not yet precise enough to predict diseases. Additionally, ethical concerns and the use of clustering and classification algorithms on small datasets limit their effectiveness. The complexity of data processing further complicates the interpretation of patient representation learning models, even though data augmentation strategies may help. Incomplete patient data also hinder model accuracy. This study aims to develop and evaluate a deep learning model that addresses these challenges. Our proposed approach is to design a disease prediction model based on deep Q-learning (DQL), which replaces the traditional Q-learning reinforcement learning algorithm with a neural network deep learning model, and the mapping capabilities of the Q-network are utilized. We conclude that the proposed model achieves the best accuracy (98%) compared with other models. Keywords: Electronic Health Records (EHRs); Deep Q-Network (DQN); disease prediction; machine learning; Deep Reinforcement Learning (DRL)