Readmission rate prediction using differential privacy technology and multilayer perceptron

Abstract

Against the backdrop of growing demands for efficient healthcare systems, balancing patient privacy with the accuracy of hospital readmission rate predictions has emerged as a prominent research issue. This study aims to address this challenge by proposing a differential privacy method that combines substitution cipher technique with noise addition. Initially, this approach anonymises medical data through substitution cipher, effectively blurring sensitive information while ensuring data integrity. Furthermore, Gaussian noise addition mechanism is utilized to maintain data privacy without excessively compromising predictive accuracy. Rigorously tested, this method demonstrated outstanding performance with an AUC of 0.8816, marking significant progress in accurately predicting hospital readmission rates while ensuring patient privacy protection. This achievement holds substantial theoretical and practical significance in the field of medical data analysis and offers new directions and methodologies for future related research.