Abstract
Prediction of cardiac disease is one the most crucial topics in the sector of medical info evaluation. The stochastic nature and the variation concerning time in electrocardiogram (ECG) signals make it burdensome to investigate its characteristics. Being evolving in nature, it requires a dynamic predictive model. With the presence of concept drift, the model performance will get worse. Thus learning algorithms require an apt adaptive mechanism to accurately handle the drifting data streams. This paper proposes an inceptive approach, Corazon Concept Drift Detection Method (Corazon CDDM), to detect drifts and adapt to them in real-time in electrocardiogram signals. The proposed methodology results in achieving competitive results compared to the methods proposed in the literature for all types of datasets like synthetic, real-world & time-series datasets.
Highlights
In the real world, data is normally nonstationary
The concept drift detection methods are classified in following categories [6]: a) sequential analysis methods - SPRT [7], PH [8] and CUSUM [8] b) error rate-based detection methods - Drift Detection Method (DDM) [9], Early Drift Detection Method (EDDM) [10], ECDD [11] and RDDM
Accurate Concept Drift Detection Method (ACDDM) gives the highest accuracy for Agrawal-60,000, Agrawal-600,000 dataset & MIT-BIH datasets, while ADaptive WINdowing (ADWIN) is performing better for Electricity dataset
Summary
In much difficult data evaluation uses, data develops throughout the period and needs to be examined in near real-time [1]. This triggers complications since the forecasts may turn much less correct as the point in time goes by or possibilities to enhance the precision may be skipped. The active approach first finds the change points in the input data streams and adapts the model, which is trigger-based adaptation. A proactive approach continuously adapts the model every time new instances arrive. The concept drift detection methods are classified in following categories [6]: a) sequential analysis methods - SPRT [7], PH [8] and CUSUM [8] b) error rate-based detection methods - DDM [9], EDDM [10], ECDD [11] and RDDM [12], c) data distribution-based detection methods - ADWIN [13] and FHDDM [14]
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