Background: A crucial issue in healthcare data analysis is the continuous growth of medical data due to the arriving of new data. Existing outlier detection techniques are capable of handling only static data and thus re-execute from scratch to identify the outliers from incremental healthcare data.
These facts create big challenges for existing outlier detection approaches in terms of their accuracies when they are implemented in an incremental fashion. This research proposes a novel approach for the identification of outliers in healthcare data by developing two layers model that can be used to improve accuracy of healthcare data analysis from business perspective.
Research question: How to develop an outliers handling approach which better adapts to incremental data and enhance data analysis in healthcare data from business perspective.
In response, the objective of this research is to provide a better understanding of the state-of-the-art techniques of anomaly detection for incremental healthcare data. an extensive experiment will be conducted on public datasets to evaluate proposed two- layers outliers detection model in improving accuracy of healthcare data analysis.
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