An improved ensemble classification-based secure two stage bagging pruning technique for guaranteeing privacy preservation of DNA sequences in electronic health records

Abstract

The advent of machine learning in the recent decade has excelled in determining new potential features and non-linear relationships existing between the data derived from the Electronic Health Records (EHR). Machine learning also enhances the process of handling data with maximum predictor variables compared to observations during the data mining process of prediction. The EHR data is often confronted with quality issues that are related to misclassification, missingness and measurement errors. In this context, ensemble classification schemes are determined to be essential for preventing the quality issues of EHR data. Moreover, the data sources like EHR include sensitive information that needs to be protected from disclosure before it is forwarded to the mining process. Further, the sensitive data of EHR must be hidden without modifying the dataset such that it does not influence the prediction accuracy of the incorporated ensemble classification mechanism. In this paper, the process of hiding EHR data is facilitated through Improved Sensitivity Drift based k-Anonymized Data Perturbation Scheme (ISD-k-ADP) that randomly perturbs the data in the dataset by including restricted amount of noise. This controlled amount of included noise is derived carefully from the Sensitivity Drift based depending on the expected privacy level before it is sent to the process of classification. This ISD-k-ADP scheme is reliable such that, it prevents the impact induced by the hidden data during the process of Two Stage Bagging Pruning based Ensemble Classification (TSBP-EC). Furthermore, the TSBP-EC uses the methods of distance and accuracy based pruning that aids in minimizing the size of the ensemble for ensuring effective and efficient classification using machine learning. The simulation results of the proposed ISD-k-ADP-TSBP-EC scheme is determined to be predominant based on Classification Accuracy, Precision, Recall and Kappa Statistic in contrast to the standard schemes.