Leveraging Machine Learning to Strengthen Network Security and Improve Threat Detection in Blockchain for Healthcare Systems

Rianat Abbas1, Victoria Abosede Ogunsanya2, Sunday Jacob Nwanyim3, Rasheed Afolabi4, Richard Kagame5, Ahmed Akinsola6, & Tosin Clement7
1Department of Information Systems, Baylor University, Texas, USA
2Cybersecurity Analyst, University of Bradford, UK
3Goizueta Business School, Emory University, Georgia, USA
4Department of Information Systems, Baylor University, Texas, USA
5Department of Information Science, Emory University, Georgia, USA
6Department of Computer Science, Austin Peay State University, Tennessee, USA
7Department of Business Analytics, University of Louisville, Kentucky, USA
DOI – http://doi.org/10.37502/IJSMR.2025.8211

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Abstract

This study investigates the integration of blockchain technology and machine learning to enhance network security and improve threat detection in healthcare systems. With healthcare systems increasingly vulnerable to cyberattacks, the study explores how blockchain’s decentralized nature can secure electronic health records (EHRs) and improve interoperability among healthcare systems. Additionally, it examines how machine learning algorithms can identify anomalies and predict potential security breaches in real time. The findings highlight key factors, such as blockchain familiarity and machine learning effectiveness, that influence the successful adoption of these technologies. The model’s evaluation metrics, including an AUC-ROC of 0.97 and accuracy of 80%, indicate that integrating blockchain and machine learning provides an effective solution for enhancing security. However, challenges such as multicollinearity, data imbalance, and integration complexities were identified. The study concludes with recommendations for addressing these challenges, emphasizing the need for continuous improvement in machine learning models, blockchain integration, and staff training to effectively safeguard healthcare systems.

Keywords: Blockchain Technology, Machine Learning, Network Security, Healthcare Systems, Threat Detection.

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