Cardiovascular Abnormalities Classification Model Using Machine Learning and Signal Processing Techniques
Keywords:
ECG, biomedical signal processing, heart rate variability, wavelet transform, PCA, Impulsive cardiac death (ISD)Abstract
Unexpected impulsive cardiac death (ISD) can occur when a person has cardiovascular illness. Electrocardiogram (ECG) signal can be used to identify impulsive cardiac mortality risks.This paper describes an intelligent human cardiac monitoring approach based on machine learning. Existing method suffers from misclassification of heart diseases. To reduce misclassification, we have proposed two innovative models for cardiovascular disease (CVD) identification. In First model, Principal component Analysis (PCA) features and Wavelet transform (WT) features are applied for machine learning classifiers such as Multinomial logistic regression (MLR) and Random Forest (RF) to find CVD. In model 2: Heart Rate Variability (HRV) and WT features are applied to Nave Bayes (NB), Decision Tree (DT) and k nearest neighbour (KNN) machine learning classifiers for classification in order to create an intelligent machine learning based cardiovascular diseases risk monitoring system. Effective features are important when Data is classified into normal or abnormal subjects. Proposed novel approach identified risks with the highest degrees of accuracy: 99.6(model 1 by MLR), and 99.3% (model 2 by DT).The outcomes demonstrate that the proposed strategy is reliable and effective for identifying impulsive cardiac risk. Effectively identifying risk factors for impulsive cardiac death is the goal of the proposed research
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