Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Kumar, Prem M.; Bhat, Archana; Bhagyalakshmi, Macherla; Nikila, G.S. Title A name given to the resource Balancing patient privacy and predictive accuracy through data anonymization in healthcare Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Advances in Computational Design;Volume;11;Issue;1;pp.47-62 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.12989/acd.2026.11.1.047" target="_blank" rel="noreferrer noopener">https://doi.org/10.12989/acd.2026.11.1.047</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105030972227?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105030972227?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kumar P.M., Willron Electronics, Bangalore, India; Bhat A., Department of Artificial Intelligence and Machine Learning, BMS Institute of Technology and Management, Bengaluru, India; Bhagyalakshmi M., School of Commerce, Finance and Accountancy, Christ University, Bangalore, India; Nikila G.S., OnGen, Bangalore, India Description An account of the resource Data anonymization in healthcare is essential for protecting sensitive patient information while enabling secure usage for research, analytics, and AI-driven clinical decision-making. In this study, the MIMIC-III - Deep Reinforcement Learning dataset was used, which contains comprehensive electronic health records (EHRs) of ICU patients. Data preprocessing was performed using Min-Max Normalization to scale numerical features and ensure consistency. Anonymization techniques such as pseudonymization, generalization, suppression, data masking, and statistical methods like k-anonymity, l-diversity, and t-closeness were applied to safeguard patient privacy. The anonymized dataset was then utilized for predictive modelling using AI techniques including Random Forest and LSTM. Results demonstrated that privacy was maintained with 0% PII leakage, while predictive accuracy remained high, achieving accuracy of 94.6%, precision of 93.8%, recall of 92.5%, and F1-score of 93.1%. This study highlights that effective data anonymization ensures compliance with HIPAA and GDPR while retaining the utility of healthcare data for advanced analytics and AI applications. 2026 Techno-Press Subject The topic of the resource AI analytics; data anonymization; GDPR; healthcare; HIPAA; k-anonymity; MIMIC-III; patient privacy; pseudonymization Publisher An entity responsible for making the resource available Techno-Press Relation A related resource ISSN: 23838477; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online