Enhancing Public Healthcare Security: Integrating Cutting-Edge Technologies into Social Medical Systems


  • Rakhi Mutha Associate Professor, Department of Information Technology, Amity University Rajasthan, Jaipur, Rajasthan, India
  • Ratnaprabha Ravindra Borhade Assistant Professor, Department of Electronics and Telecommunication, Cummins College of Engineering for Women, Pune, Maharashtra, India
  • Sheetal Sachin Barekar Assistant Professor, Department of Computer Engineering, Cummins college of engineering for women Pune, Maharashtra, India
  • Sukhvinder Singh Dari Symbiosis Law School Nagpur, Symbiosis International (Deemed University), Pune, Maharashtra, India
  • Dharmesh Dhabliya Department of Information Technology, Vishwakarma Institute of Information Technology, Pune, Maharashtra, India
  • Mukesh Patil NIT Graduate School of Management, Mahurzari, Nagpur, Maharashtra, India


Social Medical Systems, Healthcare Security, IoT-23 Analysis, Machine Learning, Deep Learning


In a time when technology is present in every aspect of our lives, it is crucial to incorporate advanced solutions to protect sensitive medical data in Social Medical Systems (SMS). This study explores the need to improve security in public healthcare by using advanced technologies to strengthen the weaknesses in the growing field of Social Medical Systems. This study specifically examines the analysis of IoT-23 data using machine learning (ML) and deep learning (DL) methods, as technology and healthcare converge. The research highlights the increasing significance of technology in healthcare, specifically focusing on the revolutionary emergence of Social Medical Systems. As these interlinked networks reshape the provision of public healthcare services, security challenges such as data breaches, cyber threats, and privacy concerns become crucial barriers that require innovative solutions. The study utilizes a wide range of machine learning (ML) and deep learning (DL) techniques to examine IoT-23 data, offering a detailed comprehension of the security environment in Social Medical Systems. The chosen models comprise Support Vector Machines (SVM), Isolation Forest, Random Forest, Convolutional Neural Networks (CNN), and Autoencoder. The results and discussions focus on evaluating metrics such as accuracy, precision, recall, and F1 score. These metrics provide insights into how effective each model is in identifying vulnerabilities and potential threats in the IoT-23 dataset. The results contribute to the wider discussion on enhancing the security of public healthcare systems. They provide suggestions for incorporating anomaly detection, encryption protocols, and continuous monitoring to strengthen the security of Social Medical Systems. This research provides guidance for policymakers, healthcare practitioners, and technologists as they navigate the changing landscape of healthcare digitization. It advocates for the proactive integration of advanced technologies to ensure the security, privacy, and accessibility of healthcare information within the interconnected web of Social Medical Systems.

DOI: https://doi.org/10.52710/seejph.483




How to Cite

Mutha, R., Borhade, R. R., Barekar, S. S., Dari, S. S., Dhabliya, D., & Patil, M. (2024). Enhancing Public Healthcare Security: Integrating Cutting-Edge Technologies into Social Medical Systems. South Eastern European Journal of Public Health, 01–11. Retrieved from https://seejph.com/index.php/seejph/article/view/483