A Multi-modal Deep Learning Approach for Predicting Type 2 Diabetes Complications: Early Warning System Design and Implementation
DOI:
https://doi.org/10.53469/wjimt.2024.07(06).15Keywords:
Type 2 Diabetes Complications, Multi-modal Deep Learning, Early Warning System, Attention MechanismAbstract
This paper presents a novel multi-modal deep learning framework for early prediction of Type 2 Diabetes (T2D) complications through an advanced early warning system. The proposed architecture integrates multiple data modalities including clinical measurements, laboratory results, and temporal patient data through a sophisticated attention-based fusion mechanism. The system implements specialized preprocessing techniques for different data modalities and employs an innovative feature extraction pipeline for comprehensive risk assessment. Experimental validation was conducted on a dataset comprising 15,847 patients collected over five years from multiple medical centres. The framework achieved 94.7% prediction accuracy with a 72-hour warning window, demonstrating superior performance compared to existing approaches. The implementation of adaptive threshold mechanisms reduced false positive rates to 4.8% while maintaining 93.8% sensitivity and 95.2% specificity. The system's effectiveness was validated through prospective testing on an independent cohort of 3,245 patients, showing robust performance across diverse patient populations. The attention-based fusion mechanism demonstrated a 15% improvement in prediction accuracy compared to conventional approaches. This research contributes to the advancement of medical artificial intelligence through interpretable deep learning models, providing healthcare practitioners with insights into the decision-making process while maintaining high prediction accuracy for early intervention in T2D complications management.
References
Tripathy, N., Moharana, B., Balabantaray, S. K., Nayak, S. K., Pati, A., & Panigrahi, A. (2024, January). A Comparative Analysis of Diabetes Prediction Using Machine Learning and Deep Learning Algorithms in Healthcare. In 2024 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC) (pp. 1-6). IEEE.
Singh, P., Silakari, S., & Agrawal, S. (2023, November). An Efficient Deep Learning Technique for Diabetes Classification and Prediction Based on Indian Diabetes Dataset. In 2023 3rd International Conference on Technological Advancements in Computational Sciences (ICTACS) (pp. 487-491). IEEE.
Bhargava, R., & Dinesh, J. (2021, October). Deep learning-based system design for diabetes prediction. In 2021 International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON) (pp. 1-5). IEEE.
Sood, K., & Sannapu, A. R. (2023, June). Risk Estimate of Complications in Type 2 Diabetes Using Ensemble Learning and Deep Learning. In 2023 IEEE World AI IoT Congress (AIIoT) (pp. 0359-0368). IEEE.
El Bouhissi, H., Al-Qutaish, R. E., Ziane, A., Amroun, K., Yaya, N., & Lachi, M. (2023, February). Towards diabetes mellitus prediction based on machine learning. In 2023 International Conference on Smart Computing and Application (ICSCA) (pp. 1-6). IEEE.
Li, L., Zhang, Y., Wang, J., & Ke, X. (2024). Deep Learning-Based Network Traffic Anomaly Detection: A Study in IoT Environments.
Cao, G., Zhang, Y., Lou, Q., & Wang, G. (2024). Optimization of High-Frequency Trading Strategies Using Deep Reinforcement Learning. Journal of Artificial Intelligence General Science (JAIGS) ISSN: 3006-4023, 6(1), 230-257.
Wang, G., Ni, X., Shen, Q., & Yang, M. (2024). Leveraging Large Language Models for Context-Aware Product Discovery in E-commerce Search Systems. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 3(4).
Li, H., Sun, J., & Ke, X. (2024). AI-Driven Optimization System for Large-Scale Kubernetes Clusters: Enhancing Cloud Infrastructure Availability, Security, and Disaster Recovery. Journal of Artificial Intelligence General Science (JAIGS) ISSN: 3006-4023, 2(1), 281-306.
Xia, S., Wei, M., Zhu, Y., & Pu, Y. (2024). AI-Driven Intelligent Financial Analysis: Enhancing Accuracy and Efficiency in Financial Decision-Making. Journal of Economic Theory and Business Management, 1(5), 1-11.
Zhang, H., Lu, T., Wang, J., & Li, L. (2024). Enhancing Facial Micro-Expression Recognition in Low-Light Conditions Using Attention-guided Deep Learning. Journal of Economic Theory and Business Management, 1(5), 12-22.
Wang, J., Lu, T., Li, L., & Huang, D. (2024). Enhancing Personalized Search with AI: A Hybrid Approach Integrating Deep Learning and Cloud Computing. International Journal of Innovative Research in Computer Science & Technology, 12(5), 127-138.
Che, C., Huang, Z., Li, C., Zheng, H., & Tian, X. (2024). Integrating generative AI into financial market prediction for improved decision-making. arXiv preprint arXiv:2404.03523.
Che, C., Zheng, H., Huang, Z., Jiang, W., & Liu, B. (2024). Intelligent robotic control system based on computer vision technology. arXiv preprint arXiv:2404.01116.
Zheng, H.; Wu, J.; Song, R.; Guo, L.; Xu, Z. Predicting Financial Enterprise Stocks and Economic Data Trends Using Machine Learning Time Series Analysis. Applied and Computational Engineering 2024, 87, 26–32.
Ju, C., & Zhu, Y. (2024). Reinforcement Learning‐Based Model for Enterprise Financial Asset Risk Assessment and Intelligent Decision‐Making.
Huang, D., Yang, M., & Zheng, W. (2024). Integrating AI and Deep Learning for Efficient Drug Discovery and Target Identification.
Yang, M., Huang, D., & Zhan, X. (2024). Federated Learning for Privacy-Preserving Medical Data Sharing in Drug Development.
Tian, Q., Wang, Z., Cui, X. Improved Unet brain tumor image segmentation based on GSConv module and ECA attention mechanism. arXiv preprint arXiv:2409.13626.
Li, H., Wang, G., Li, L., & Wang, J. (2024). Dynamic Resource Allocation and Energy Optimization in Cloud Data Centers Using Deep Reinforcement Learning. Journal of Artificial Intelligence General Science (JAIGS) ISSN: 3006-4023, 1(1), 230-258.
Ma, X., Wang, J., Ni, X., & Shi, J. (2024). Machine Learning Approaches for Enhancing Customer Retention and Sales Forecasting in the Biopharmaceutical Industry: A Case Study. International Journal of Engineering and Management Research, 14(5), 58-75.