Year: 2025
Type: article
Abstract: In an era of increasing cyber threats against healthcare institutions, medical pharmacies are emerging as critical yet vulnerable components of the digital health ecosystem.This study presents a comprehensive machine learning-based framework for predicting cybersecurity risk in pharmacy environments using operational, threat, and control-related features.We evaluated the predictive performance of three regression models Linear Regression, Support Vector Regressor (SVR), and Random Forest Regressor, using metrics such as R² score, RMSE, and MAE.Random Forest outperformed all models with an R² of 0.91, RMSE of 0.42, and MAE of 0.28, confirming its superiority in capturing non-linear relationships within pharmacy operations.For binary risk classification, the Random Forest Classifier achieved an AUC of 1.00, with a confusion matrix showing high precision (91.4%) and recall (87.6%).Feature importance analysis revealed that control effectiveness, threat probability, and asset value were the most influential factors affecting cybersecurity risk scores.These
Author: Olanrewaju Ogundojutimi
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4410361978