Deep Learning-Enhanced Association Rule Mining for Disease Diagnosis and Treatment Planning

This chapter presents a hybrid deep learning–enhanced association rule mining (DL–ARM) framework for disease diagnosis, treatment planning, and clinical decision support. Traditional ARM methods provide interpretability but struggle with high-dimensional medical data, while deep learning models excel at feature extraction yet lack transparency. The proposed framework combines DL for latent feature representation withARMto generate clinically meaningful, explainable rules. Case studies on electronic health records and physiological datasets demonstrate improved predictive performance, rule density, and interpretability compared to standalone DL or conventional ARM. The chapter also highlights future directions, including federated and privacy-preserving rule mining, GAN-based synthetic rule generation, and quantum-enhancedanalytics, offering a scalable, interpretable, and high-performance approach for modern healthcare analytics. Copyright © 2026, IGI Global Scientific Publishing. Copying or distributing in print or electronic forms without written permission of IGI Global Scientific Publishing is prohibited. Use of this chapter to train generative artificial intelligence (AI) technologies is expressly prohibited. The publisher reserves all rights to license its use for generative AI training and machine learning model development.