Explainable Artificial Intelligence For Predictive Healthcare Analytics : A Data-Driven Framework For Clinical Decision Support
DOI:
https://doi.org/10.29284/zdfexk19Keywords:
Explainable Artificial Intelligence (XAI), Predictive Healthcare Analytics (PHA), Convolutional Neural Network–Recurrent Neural Network (CNN–RNN) Hybrid Model, Clinical Decision Support System (CDSS).Abstract
This article proposes an Explainable Artificial Intelligence (XAI)-driven predictive healthcare analytics framework for clinical decision support using structured Electronic Health Records and temporal ICU data. The methodology integrates anomaly-aware preprocessing, Convolutional Neural Networks for spatial feature extraction, Recurrent Neural Networks for longitudinal modeling, and SHAP-based interpretability mechanisms to enhance transparency and clinical trust. Experimental validation demonstrates superior predictive performance, achieving 92% accuracy and 0.94 AUC in ICU outcome prediction tasks. The hybrid architecture effectively captures both static clinical biomarkers and dynamic physiological progression patterns. Explainability analysis identifies lactate, heart rate, and white blood cell count as dominant predictive contributors. The proposed framework delivers robust, interpretable, and clinically actionable decision support suitable for real-world healthcare deployment.
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This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
