Balancing patient privacy and predictive accuracy through data anonymization in healthcare
- Title
- Balancing patient privacy and predictive accuracy through data anonymization in healthcare
- Creator
- Kumar, Prem M.; Bhat, Archana; Bhagyalakshmi, Macherla; Nikila, G.S.
- Description
- 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
- Source
- Advances in Computational Design;Volume;11;Issue;1;pp.47-62
- Date
- 01-01-2026
- Publisher
- Techno-Press
- Subject
- AI analytics; data anonymization; GDPR; healthcare; HIPAA; k-anonymity; MIMIC-III; patient privacy; pseudonymization
- Coverage
- 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
- Rights
- Restricted Access; Hardcopy may be available in the library
- Relation
- ISSN: 23838477;
- Format
- online
- Language
- English
- Type
- Article
Collection
Citation
Kumar, Prem M.; Bhat, Archana; Bhagyalakshmi, Macherla; Nikila, G.S., “Balancing patient privacy and predictive accuracy through data anonymization in healthcare,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 20, 2026, https://archives.christuniversity.in/items/show/23216.