Secure Healthcare Data Analytics for Early Disease Detection: Cybersecurity and Big Data Solutions
Abstract
The fusion of big data analytics and Cybersecurity has transformed the healthcare industry through technology-enabled methods to detect diseases early and achieve clinical improvement. The exponential growth of electronic health records (EHRs), wearable sensors, and Internet of Things (IoT)-based medical devices has created vast amounts of patient data available for analytics. This data can be used to identify early symptoms of a disease and inform evidence-based clinical decisions. However, increased dependence on digital technologies creates new challenges including data privacy risk; cyberattacks; and unpermitted access to data. This article looks at secure methods for healthcare data analytics leveraging big data technologies in conjunction with advanced cybersecurity methods, including encryption, blockchain, federated learning, and artificial intelligence (AI) models which help ensure patient data remain protected while providing predictive information. The findings outline the vital importance of ensuring innovation with protection of privacy and presents a broadly applicable framework for secure, scalable, and ethical analytics for healthcare.