Differential privacy based classification model for mining medical data stream using adaptive random forest

Most typical data mining techniques are developed based on training the batch data which makes the task of mining the data stream represent a significant challenge. On the other hand, providing a mechanism to perform data mining operations without revealing the patient's identity has increasing importance in the data mining field. In this work, a classification model with differential privacy is proposed for mining the medical data stream using Adaptive Random Forest (ARF). The experimental results of applying the proposed model on four medical datasets show that ARF mostly has a more stable performance over the other six techniques.

Automated summary

A classification model with differential privacy is proposed for mining medical data streams using Adaptive Random Forest (ARF). Experimental results demonstrate that ARF generally shows more stable performance compared to the other six techniques when applied to four medical datasets.