Content Based Secure Recommender System for Big Data Analytics

Abstract

In today’s digital era, recommender systems play a pivotal role in enhancing user experiences across various domains, including healthcare. Big data analytics in healthcare refers to the use of advanced data analysis techniques to extract meaningful insights, patterns, and knowledge from large and complex healthcare datasets. Recommender systems play a valuable role in the context of big data analytics in healthcare by helping to make sense of the vast amount of data available and improving the quality of healthcare services. Recommender systems assist healthcare professionals by providing evidence-based recommendations for diagnosis and treatment. Security is a critical issue in big data analytics in healthcare due to the sensitive nature of healthcare data. Healthcare data often includes personally identifiable information (PII), and unauthorized access to this information can lead to identity theft and privacy breaches. Implementing strict access controls and encryption measures is essential. Hence, this paper proposed the Content Service Ensemble Recommender System (CSERS) model uses the content based collaborative filtering with the blowfish algorithm for the security features. The proposed CSERS model uses the big data analytics model with the tokened boost stamping feature extraction model with the computation of polarity. The secure recommednder model for the healthcare monitoring of the patient in healthcare application is evaluated for the computation of satisfaction level of patients. To achieve the desire security in the Heakthcare data Blowfish cryptographic process is implemented for the security. Initially, the performance of the proposed CSERS’ scalability by examining its performance across different dataset sizes and transaction volumes. The findings reveal CSERS’ ability to efficiently process data loads of varying magnitudes, making it adaptable to the demands of real-world healthcare environments. Second, the paper CSERS’ security assessment capabilities, highlighting its proactive approach to security level estimation based on file size. This feature enhances data integrity and user trust, critical considerations in healthcare content recommendation systems. Furthermore, the research investigates CSERS’ sentiment analysis capabilities, showcasing strong correlations between sentiment aspects such as professionalism, communication, and overall care. These correlations align with the system’s target sentiment, indicating its effectiveness in tailoring recommendations to meet users’ preferences and sentiments. Lastly, the study evaluates CSERS’ performance in attack classification, where it excels by consistently achieving high accuracy, precision, recall, and ROC-AUC percentages. For instance, CSERS achieves an accuracy of 97.8% for datasets of 50 MB, underscoring its reliability in identifying security threats accurately. In conclusion, this paper underscores CSERS as a versatile and powerful system for content recommendation in healthcare applications. Its ability to enhance user experiences while ensuring security and trustworthiness makes it a valuable asset in today’s data-driven healthcare landscape.