Key result
Machine learning using HRV spectral features predicts congestive heart disease and CAD.
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A machine learning approach combining spectral analysis, generalized discriminant analysis, and extreme learning machine was proposed for classifying cardiac diseases using HRV signals.
This study presents combination of spectral analysis method, generalized discriminant analysis (GDA), and the extreme learning machine (LELM) classifier algorithms which provide a robust method for prediction of cardiac diseases. Firstly, spectral approaches, has been applied for features extraction from heart rate variability (HRV) signal. The proposed GDA +ELM model was fitted with the features derived from approaches for the classification model of congestive heart disease and coronary artery disease. The standard database of HRV signal was collected from Physio net ATM's HRV source for training and validating predictive models for cardiac diseases.
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Tiwari et al. (2024) studied Congestive heart disease and coronary artery disease. GDA + ELM model with spectral features was evaluated on Prediction of cardiac diseases. A machine learning model combining generalized discriminant analysis and extreme learning machine with spectral features from HRV signals was developed to predict cardiac diseases.
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