AI-optimized GRU-based self-attention model for predictive diabetes staging in IoT healthcare 5.0

Abstract

In the Healthcare 5.0 environment, the IoT devices are used for collecting the users statics. Hence, IoT devices can be used for the early detection and staging of diabetes. However, due to the complex interrelationship among the healthcare feature-set it is difficult to do an accurate prediction. In this context, this paper presents self-attention GRU model for predictive diabetes detection. A GRU-based self-attention mechanism captures temporal dependencies and spatial features that improves the model performance. Finally, CNN with Batch Normalization and ReLU performs the final classification. Experimental results show that the model achieved 93.94% accuracy, 95.28% precision, 93.94% recall, and an AUC of 0.9697, outperforming GRU, LSTM, RNN, and transformer-based baselines.

Affiliated Institutions

• Jiading District Central Hospital CN
• University of Petroleum and Energy Studies IN
• Northern Border University SA
• Chandigarh University IN
• University of Business and Technology SA
• Shanghai University of Medicine and Health Sciences CN
• Princess Nourah bint Abdulrahman University SA
• Asia University TW

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