Multi-Surrogate-Enhanced Deep Neural Network for Dynamic Medical Decision Support
DOI:
https://doi.org/10.56286/8n21kv40Keywords:
Medical Decision Support Systems (MDSS), Deep Learning Integration, Surrogate ModelingAbstract
Medical Decision Support Systems (MDSSs) have become indispensable tools in modern healthcare, assisting physicians and healthcare professionals in disease diagnosis, treatment planning, patient monitoring, and clinical decision-making. The rapid growth of artificial intelligence (AI) and deep learning has significantly enhanced the capabilities of these systems by enabling accurate analysis of complex medical data. However, conventional deep learning models often require extensive computational resources, large training datasets, and long processing times, limiting their application in real-time clinical environments. To address these challenges, this study introduces a Multi-Surrogate-Enhanced Deep Neural Network (MSEDNN) model that integrates surrogate modeling techniques, including Radial Basis Functions (RBF) and Support Vector Regression (SVR), with deep neural network architectures. The proposed framework improves feature extraction, reduces computational complexity, enhances real-time adaptability, and supports personalized clinical decision-making across multiple medical tasks. The performance of the proposed model was evaluated using several benchmark medical datasets involving diagnostic classification and medical imaging applications. Experimental results demonstrated that the MSEDNN model achieved an average classification accuracy of 96.5%, with a 97.0% sensitivity and an F1-score of 95.6%, indicating excellent diagnostic performance and reliability. Furthermore, the framework attained 97.2% accuracy on medical imaging datasets while maintaining a low inference latency of 140 ms, making it suitable for time-sensitive clinical applications. The incorporation of surrogate-based preprocessing also reduced model training time by approximately 25%, significantly improving computational efficiency without compromising predictive performance. These findings demonstrate that the proposed MSEDNN framework provides an effective, accurate, and computationally efficient solution for intelligent medical decision support. By combining surrogate modeling with deep learning, the model offers enhanced diagnostic accuracy, faster response times, and improved adaptability, highlighting its potential for integration into next-generation healthcare systems and precision medicine applications.
Downloads
References
Kose U, Deperlioglu O, Alzubi J, Patrut B. Deep learning for medical decision support systems. 2021.
Hawezi RS, Khoshaba FS, Kareem SW. A comparison of automated classification techniques for image processing in video internet of things. Computers and Electrical Engineering. 2022;101:108074.
Jader RF, Kareem SW, Awla HQ. Ensemble deep learning technique for detecting MRI brain tumor. Applied Computational Intelligence and Soft Computing. 2024;2024(1):6615468.
Masood A, Yang P, Sheng B, Li H, Li P, Qin J, et al. Cloud-based automated clinical decision support system for detection and diagnosis of lung cancer in chest CT. IEEE Journal of Translational Engineering in Health and Medicine. 2019;8:1-13.
Shao Z, Dou W, Pan Y. Dual-level deep evidential fusion: Integrating multimodal information for enhanced reliable decision-making in deep learning. Information Fusion. 2024;103:102113.
Kwasigroch A, Jarzembinski B, Grochowski M. Deep CNN based decision support system for detection and assessing the stage of diabetic retinopathy. In: 2018 International Interdisciplinary PhD Workshop (IIPhDW); 2018 May. p. 111-116.
Lakshmanaprabu SK, Mohanty SN, Krishnamoorthy S, Uthayakumar J, Shankar K. Online clinical decision support system using optimal deep neural networks. Applied Soft Computing. 2019;81:105487.
Cai CJ, Reif E, Hegde N, Hipp J, Kim B, Smilkov D, et al. Human-centered tools for coping with imperfect algorithms during medical decision-making. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems; 2019 May. p. 1-14.
Seo H, Bassenne M, Xing L. Closing the gap between deep neural network modeling and biomedical decision-making metrics in segmentation via adaptive loss functions. IEEE Transactions on Medical Imaging. 2020;40(2):585-593.
Pavlova M, Terhljan N, Chung AG, Zhao A, Surana S, Aboutalebi H, et al. COVID-Net CXR-2: An enhanced deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. Frontiers in Medicine. 2022;9:861680.
Yoon K, Kim JY, Kim SJ, Huh JK, Kim JW, Choi Y. Explainable deep learning-based clinical decision support engine for MRI-based automated diagnosis of temporomandibular joint anterior disk displacement. Computer Methods and Programs in Biomedicine. 2023;233:107465.
Chen W, Long G, Yao L, Sheng QZ. AMRNN: Attended multi-task recurrent neural networks for dynamic illness severity prediction. World Wide Web. 2020;23:2753-2770.
Shukla U, Saxena GJ, Kumar M, Bafila AS, Pundir A, Singh S. An improved decision support system for identification of abnormal EEG signals using a 1D convolutional neural network and Savitzky-Golay filtering. IEEE Access. 2021;9:163492-163503.
Aalsaud A, Kareem SW, Yousif RZ, Mohammed AS. Ensemble transfer learning for botnet detection in the Internet of Things. Scalable Computing: Practice and Experience. 2024;25(5):4312-4322.
Alsuhibany SA, Abdel-Khalek S, Algarni A, Fayomi A, Gupta D, Kumar V, et al. Ensemble of deep learning based clinical decision support system for chronic kidney disease diagnosis in medical Internet of Things environment. Computational Intelligence and Neuroscience. 2021;2021(1):4931450.
Yaseliani M, Maghsoodi AI, Hassannayebi E, Aickelin U. Diagnostic clinical decision support based on deep learning and knowledge-based systems for psoriasis: From diagnosis to treatment options. Computers & Industrial Engineering. 2024;187:109754.
Senkamalavalli R, Sankar S, Parivazhagan A, Raja R, Yoganand S, Srinivas P, et al. Enhancing clinical decision-making with cloud-enabled integration of image-driven insights. Indonesian Journal of Electrical Engineering and Computer Science. 2024;36(1):338-346.
Shuford J. Deep reinforcement learning unleashing the power of AI in decision-making. Journal of Artificial Intelligence General Science. 2024;1(1).
Wu Y, Chen B, Cai HH, Wang D, Yuan Q. Evolutionary game theoretic approach with deep learning for health decision-making in critical environment. Annals of Operations Research. 2024:1-16.
Alijoyo FA, Janani S, Santosh K, Shweihat SN, Alshammry N, Ramesh JVN, et al. Enhancing AI interpretation and decision-making: Integrating cognitive computational models with deep learning for advanced uncertain reasoning systems. Alexandria Engineering Journal. 2024;99:17-30.
Fong SJ, Li G, Dey N, Crespo RG, Herrera-Viedma E. Composite Monte Carlo decision making under high uncertainty of novel coronavirus epidemic using hybridized deep learning and fuzzy rule induction. Applied Soft Computing. 2020;93:106282.
Giger ML. Machine learning in medical imaging. Journal of the American College of Radiology. 2018;15(3):512-520.
Park J, Rho MJ, Moon MH. Enhanced deep learning model for precise nodule localization and recurrence risk prediction following curative-intent surgery for lung cancer. PLoS One. 2024;19(7):e0300442.
Jayatilake SMDAC, Ganegoda GU. Involvement of machine learning tools in healthcare decision making. Journal of Healthcare Engineering. 2021;2021(1):6679512.
Gavade AB, Nerli R, Kanwal N, Gavade PA, Pol SS, Rizvi STH. Automated diagnosis of prostate cancer using mpMRI images: A deep learning approach for clinical decision support. Computers. 2023;12(8):152.
Jiang X, Feng C, Sun W, Feng L, Hao Y, Liu Q, et al. Enhancing clinical decision-making in endometrial cancer through deep learning technology: A review of current research. Digital Health. 2024;10:20552076241297053.
Al Reshan MS, Amin S, Zeb MA, Sulaiman A, Alshahrani H, Shaikh A, et al. An innovative ensemble deep learning clinical decision support system for diabetes prediction. IEEE Access. 2024.
Çavdar T, Ebrahimpour N, Kakız MT, Günay FB. Decision-making for the anomalies in IIoTs based on 1D convolutional neural networks and Dempster-Shafer theory. The Journal of Supercomputing. 2023;79(2):1683-1704.
Venkatasubramanain S, Kumar RM, MohanaDevi A. Explainable deep learning models for healthcare decision support. International Journal. 2023;12(10).
Saranya SS, Anusha P, Chandragandhi S, Kishore OK, Kumar NP, Srihari K. Enhanced decision-making in healthcare cloud-edge networks using deep reinforcement and lion optimization algorithm. Biomedical Signal Processing and Control. 2024;92:105963.
Kleinman M, Chandrasekaran C, Kao JC. Recurrent neural network models of multi-area computation underlying decision-making. bioRxiv. 2019:798553.
Liu B, Chi W, Li X, Li P, Liang W, Liu H, et al. Evolving the pulmonary nodules diagnosis from classical approaches to deep learning-aided decision support: Three decades' development course and future prospect. Journal of Cancer Research and Clinical Oncology. 2020;146:153-185.
Kollias D, Arsenos A, Kollias S. A deep neural architecture for harmonizing 3-D input data analysis and decision making in medical imaging. Neurocomputing. 2023;542:126244.
Lin B, Bouneffouf D, Cecchi G. Predicting human decision making in psychological tasks with recurrent neural networks. PLoS One. 2022;17(5):e0267907.
Deva Hema D, Kumar KA. Optimized deep neural network based intelligent decision support system for traffic state prediction. International Journal of Intelligent Transportation Systems Research. 2023;21(1):26-35.
Kumar SA, Ananda Kumar TD, Beeraka NM, Pujar GV, Singh M, Narayana Akshatha HS, et al. Machine learning and deep learning in data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry. Future Medicinal Chemistry. 2022;14(4):245-270.
Ahmed AS, Salah HA, Layeb SB. Data mining techniques used for evaluation an efficient DDoS attack detection system: A deep-learning model. NTU Journal of Pure Sciences. 2024;3(3):17-29.
Abdulatif FM, Ali AW. Review of classification medical images using ensemble learning. NTU Journal of Pure Sciences. 2024;3(4):13-20.
Downloads
Published
License
Copyright (c) 2026 Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
How to Cite
