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

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.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2025-12-09
DOI
https://doi.org/10.1038/s41598-025-29674-z
Primary Topic
Artificial Intelligence in Healthcare
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Razaz Waheeb Attar, Akshat Gaurav, Brij B. Gupta, Varsha Arya et al.
Scientific Reports
Artificial Intelligence in Healthcare
article

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

Razaz Waheeb Attar, Akshat Gaurav, Brij B. Gupta, Varsha Arya, Ching‐Hsien Hsu, Ahmed Alhomoud, Liang Zhou
article en

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.

Scientific ReportsVol. 16(1)
Chandigarh University (IN), Princess Nourah bint Abdulrahman University (SA), Northern Border University (SA), Asia University (TW), China Medical University (TW), Symbiosis International University (IN), Kyung Hee University (KR), Jiading District Central Hospital (CN), Korea Soongsil Cyber ​​University (KR), China Medical University Hospital (TW), Ronin Institute (US), University of Petroleum and Energy Studies (IN)
Openalex Percentile: Top 53%
Artificial Intelligence in Healthcare
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.