Regularized HessELM and Inclined Entropy Measurement for Congestive Heart Failure Prediction

Apdullah Yayık; Yakup Kutlu; Gökhan Altan

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arXiv · 1907.05888

Regularized HessELM and Inclined Entropy Measurement for Congestive Heart Failure Prediction

Apdullah Yayık·Yakup Kutlu·Gökhan Altan

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Abstract

Our study concerns with automated predicting of congestive heart failure (CHF) through the analysis of electrocardiography (ECG) signals. A novel machine learning approach, regularized hessenberg decomposition based extreme learning machine (R-HessELM), and feature models; squared, circled, inclined and grid entropy measurement were introduced and used for prediction of CHF. This study proved that inclined entropy measurements features well represent characteristics of ECG signals and together with R-HessELM approach overall accuracy of 98.49% was achieved.

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Apdullah Yayık, Yakup Kutlu, Gökhan Altan. 2019-07-12. Regularized HessELM and Inclined Entropy Measurement for Congestive Heart Failure Prediction. https://arxiv.org/abs/1907.05888

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