Securing The Future Of Healthcare: Building A Resilient Defense System For Patient Data Protection

TL;DR

This paper proposes a machine learning-based defense system to predict and mitigate healthcare data breaches, emphasizing the importance of robust network security for protecting patient information.

Contribution

It introduces a gradient boosting classifier model for predicting breach severity and recommends comprehensive security protocols for healthcare data protection.

Findings

Hacking and IT incidents are the most common breaches.

Network servers are the primary targets.

Gradient boosting performs well in breach severity prediction.

Abstract

The increasing importance of data in the healthcare sector has led to a rise in cybercrime targeting patient information. Data breaches pose significant financial and reputational risks to many healthcare organizations including clinics and hospitals. This study aims to propose the ideal approach to developing a defense system that ensures that patient data is protected from the insidious acts of healthcare data threat actors. Using a gradientboosting classifier machine learning model, the study predicts the severity of healthcare data breaches. Secondary data was collected from the U.S. Department of Health and Human Services Portal with key indicators. Also, the study gathers key cyber-security data from Kaggle, which was utilized for the study. The findings revealed that hacking and IT incidents are the most common type of breaches in the healthcare industry, with network servers being targeted in most cases. The model evaluation showed that the gradient boosting algorithm performs well. Therefore, the study recommends that organizations implement comprehensive security protocols, particularly focusing on robust network security to protect servers