Key result
The proposed Explainable AI-empowered LSTM framework achieved 99% accuracy in healthcare anomaly detection, outperforming traditional machine learning models.
Why the study?
Existing healthcare systems using traditional AI methods face lack of interpretability, data privacy risks, and inefficiencies in real-time decision-making, hindering user trust and clinical adoption.
Absolute Event Rate: 99% vs 92.4%
An XAI-empowered LSTM-based edge healthcare framework achieved 99% accuracy in anomaly detection, demonstrating potential for secure and interpretable real-time healthcare analytics.
The Infusion of Explainable Artificial Intelligence (XAI) in secured edge-based healthcare systems addresses the critical challenges of ensuring trust, transparency, and security in sensitive medical applications. Existing healthcare systems leveraging traditional AI methods often face issues such as lack of interpretability, data privacy risks, and inefficiencies in real-time decision-making. These limitations hinder user trust and the adoption of AI solutions in clinical and edge environments. To overcome these challenges, we propose an XAI-empowered secured edge-based healthcare framework utilizing deep learning (DL) models, specifically Long Short-Term Memory (LSTM) networks, for accurate and interpretable diagnosis. The system incorporates the UNSW dataset to train and validate the model for healthcare anomaly detection and prediction tasks. By embedding XAI methodologies, the proposed framework ensures that decision-making processes are transparent and understandable to healthcare professionals, fostering trust and enabling better clinical decision-making. Our implementation addresses the critical need for secure and real-time healthcare analytics at the edge while maintaining high accuracy and privacy. Through rigorous experimentation, the proposed system achieves a remarkable accuracy of 99%, demonstrating its potential to revolutionize edge-based healthcare solutions. This research highlights the synergy between XAI, edge computing, and DL techniques in advancing secured and interpretable healthcare systems for real-world applications.
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S et al. (2024) studied Heart disease and healthcare anomaly detection. LSTM-based Explainable AI framework vs. Traditional machine learning models (GRU, RF, DT, SVM) was evaluated on Classification accuracy. The proposed Explainable AI-empowered LSTM framework achieved 99% accuracy in healthcare anomaly detection, outperforming traditional machine learning models.
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