  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
