DESIGN AND DEVELOPMENT OF HEALTHCARE FRAMEWORK USING DIGITAL TWIN

Abstract

The integration of Digital Twin (DT) technology in healthcare has paved the way for significant advancements in patient care, security, and disease detection. This compilation of four research studies presents a holistic view of the evolving role of DT in healthcare, emphasizing its applications in security, artificial intelligence-driven diagnostics, and personalized treatment frameworks. The studies collectively highlight the importance of secure and efficient healthcare ecosystems leveraging machine learning, blockchain, and deep learning architectures. The first study explores the role of DT in healthcare security through a Metaverse-DT-based framework, addressing privacy concerns and data protection challenges. The study outlines how Internet of Things (IoT) sensors enable real-time data collection for personalized digital models, enhancing patient monitoring and decision-making. Blockchain integration within DT provides an additional layer of security, ensuring reliable simulation environments for healthcare applications. The second study presents an automated DT framework for cervical cancer detection using the CervixNet classifier model. The proposed model, employing machine learning and deep learning techniques, demonstrates exceptional performance in diagnosing cervical abnormalities. Utilizing the SIPaKMeD dataset, the model achieves a classification accuracy of 98.91% with support vector machines (SVM), underscoring the potential of DT in enhancing diagnostic precision and supporting clinical decision-making. The third study investigates the security of IoT networks in healthcare through a DT framework integrating Elliptic Curve Cryptography (ECC) and blockchain. By employing a Genetic Algorithm-Optimized Random Forest (GAO-RF) model for intrusion detection, the system enhances the safety of healthcare data while maintaining scalability and efficiency. The proposed model achieves high accuracy rates (98.4% detection accuracy, 97.3% F1-score), demonstrating its robustness in mitigating cybersecurity threats in healthcare IoT environments. The fourth study introduces the Monkeypox Skin Lesion Detector Network (MxSLDNet) within a DT framework for automated early detection of monkeypox. The model, tested on the "Monkeypox Skin Lesion Dataset," surpasses traditional pre-trained deep-learning architectures such as VGG-19, ResNet-101, and DenseNet-121 in terms of precision, recall, and accuracy. MxSLDNet achieves an vii accuracy of 95.67%, addressing the critical need for a lightweight, storage-efficient, and scalable solution for infectious disease detection in resource-limited healthcare settings. By synthesizing insights from these studies, this research underscores the transformative potential of DT in various healthcare domains. The integration of AI- driven models, blockchain security mechanisms, and digital simulation frameworks fosters a secure, intelligent, and scalable healthcare ecosystem. Future advancements in DT will likely focus on expanding real-time clinical decision support systems, enhancing interoperability with electronic health records (EHRs), and integrating federated learning for secure, large-scale data processing. The findings provide a strong foundation for the continued exploration of DT in revolutionizing digital healthcare.