International Journal of Science, Engineering and TechnologyAuthors: Akash S, Karthikrajan G
Abstract: Early disease prediction using deep learning models has revolutionized healthcare analytics by achieving state-of-the-art accuracy in analyzing complex, multimodal data sources, including sequential electronic health records (EHRs) such as time-series vital signs, laboratory results, medication histories, and demographic profiles, alongside high-resolution medical imaging modalities like chest X-rays, MRIs, and CT scans. Despite these advancements, the clinical deployment of such models faces significant barriers stemming from their opaque "black-box" decision-making processes, which provide no insight into the underlying reasoning for predictions, thereby undermining clinician trust, increasing diagnostic hesitation, and posing challenges to regulatory compliance with stringent data protection laws, including the European Union's General Data Protection Regulation (GDPR) "right to explanation" and the U.S. Health Insurance Portability and Accountability Act (HIPAA). This paper proposes an innovative hybrid explainable artificial intelligence (XAI) framework, seamlessly integrated with scalable cloud computing infrastructure, to deliver both exceptional predictive accuracy and unprecedented clinical interpretability for early disease detection—targeting conditions like sepsis, cardiovascular events, and pneumonia. The core architecture synergistically combines Temporal Convolutional Networks (TCN), which excel at modelling long-range temporal dependencies in EHR sequences through dilated convolutions and residual connections, with Convolutional Neural Networks (CNN) variants such as ResNet-50 for robust spatial feature extraction from imaging data. To address privacy concerns in multi-institutional settings, the model leverages a federated transfer learning approach, enabling decentralized training where model updates are aggregated without exchanging raw patient data, thus minimizing risks of data breaches while harnessing diverse datasets. Interpretability is embedded via a comprehensive dual-explanation strategy: globally, Shapley Additive explanations (SHAP) compute feature attribution scores to reveal dataset-wide importance hierarchies (e.g., prioritizing elevated troponin levels over age in cardiac risk prediction); locally, Local Interpretable Model-agnostic Explanations (LIME) approximate model behaviour with surrogate linear models for EHR inputs, complemented by Gradient-weighted Class Activation Mapping (Grad-CAM) heatmaps that visually pinpoint salient regions in medical images, such as pulmonary opacities indicative of pneumonia. Deployed on elastic cloud platforms like Amazon Web Services (AWS) EC2 instances orchestrated with Kubernetes for auto-scaling and Apache Kafka for real-time data streaming, the system ensures low-latency inference suitable for hospital edge computing.
