A Secure Predictive Framework for Preventing Health Care Data

Abstract

The incorporation of Artificial intelligence (AI) with online resources in the healthcare sector has significantly enhanced medical services. Facilitating accurate diagnoses, tailored treatment strategies, and ongoing patient surveillance, Internet of Things (IoT) devices promote efficient communication, while AI analyzes detailed healthcare data to improve decision-making and reduce costs. Nevertheless, securing data storage and transmission poses a significant challenge, especially as the threat of data breaches and cyber-attacks increases. Protecting patient privacy and securing health records is essential to maintaining public health. To address these challenges, a new approach called Butterfly Optimization Based Modular Neural Network (BObMNN) has been developed, focusing on data security and predictive performance. The healthcare database was first assembled and imported into a Python environment for further analysis. Following this, data security protocols were established using encryption and decryption methods. The encrypted data was then subjected to preprocessing, specifically feature selection, where butterfly optimization (BOA) was utilized to determine the most important attributes for predictive analysis. The constructed model was evaluated using a variety of measures, including Area under the Curve (AUC), accuracy, Recall, F-score, and Precision.