NDLI: Cardiac Health Assessment Using ANN in Diabetic Population

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Cardiac Health Assessment Using ANN in Diabetic Population

Content Provider	Scilit
Author	Joshi, Manjusha Desai, K. D. Menon, M. S. Verlekar, Harish
Copyright Year	2021
Description	Heart rate variability (HRV) analysis is an authentic tool for cardiac risk stratification and validates the HRV parameters with echo-cardiogram analysis. The HRV indices derived from electrocardiographs (ECG) acquired for 3–5 minutes of disease population. Statistical independence is ensured by t-test. Heart rate, standard deviation of NN intervals (SDNN) and high frequency (HF) power are the candidate features. The classifier uses the Error Back Propagation algorithm with the Artificial Neural Network (ANN). Cluster analysis (k-NN) is based on HRV parameters. Classifier accuracy is 85 percent for 47 epochs with 30 hidden layer neurons. Mean square error (MSE) is 0.000001 at 41st epoch. False positive error is 0 percent and false negative error is 85.4 percent. The results of the cluster analysis are validated with Left Ventricular Ejection Fraction (LVEF). Four clusters are identified based upon the quantitative analysis of feature sets, based on the risk. Validation of the maximum cluster is cross checked by the LVEF. Book Name: Disruptive Trends in Computer Aided Diagnosis
Related Links	https://api.taylorfrancis.com/content/chapters/edit/download?identifierName=doi&identifierValue=10.1201/9781003045816-7&type=chapterpdf
DOI	10.1201/9781003045816-7
Language	English
Publisher	Informa UK Limited
Publisher Date	2021-08-11
Access Restriction	Open
Subject Keyword	Book Name: Disruptive Trends in Computer Aided Diagnosis Artificial Neural Network Diabetic Percent Cardiac
Content Type	Text
Resource Type	Chapter

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