IPMOD: An efficient outlier detection model for high-dimensional medical data streams

Highlights：

• We designed a new real-time outlier detection algorithm for multi-dimensional medical data streams.

• The anomaly detection algorithm proposed in this paper does not need to consider various prior information of the data stream (such as feature distribution or other density information).

• The weighting scheme designed in this paper for high-dimensional data streams can accurately distinguish the impact of different attributes on detection accuracy.

• We have designed a new pruning strategy that can greatly reduce the time consumption of the algorithm.