An Explainable Hybrid CNN–LSTM Framework with Federated Learning for Privacy-Preserving Healthcare Analytics

AI is transforming healthcare in the digital age, providing robust resources for medical diagnosis, clinical forecasting, and patient care monitoring. One of the challenges to trust is that many Deep Learning (DL) models are "black boxes" and do not provide transparency of how the decision was made. This opacity will make it difficult to depend on automation outputs. What complicates matters even more is the fact that healthcare data is highly diverse, drawn from a multitude of sources including medical scans, digital health records, and time-stamped clinical notes, making it difficult to combine and analyze. Sensitive patient data also poses privacy barriers to institutions to easily collaborate. This study presents a multi-modal DL approach that is explainable and privacy-preserving for intelligent healthcare analysis, addressing these inter-related challenges. The methodology utilizes Convolutional Neural Networks (CNNs) to pick out spatial patterns, Long Short-Term Memory (LSTM) networks to process sequences of clinical data, and Transformer-based attention mechanisms to intelligently fuse these different types of information. We have incorporated tools such as Explainable AI (XAI), Grad-CAM, SHAP, and LIME to bring a visual and feature-based explanation to each prediction, opening the door to the decision-making process. Further, in a federated learning environment, the model can be trained at several institutions without any raw patient data leaving the home server. In fact, the experimental results demonstrates that the framework achieves the accuracy of 98.32%, precision of 98.29%, recall of 98.43%, and F1 score of 98.36%, which are all better than the conventional deep learning techniques. In conclusion, this design enhances the interpretability, reliability, privacy, and overall generalizability, opening the door for AI systems that are reliable and scalable towards clinical application.