Performance Assessment of Ensemble Learning Model for Prediction of Cardiac Disease Among Smokers Based on HRV Features
2021 ◽
Vol 10
(1)
◽
pp. 19-34
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Smoking impacts the pattern of heart rate variability (HRV); HRV therefore acts as a predictor of cardiac diseases (CD). In this study, to predict CD non-invasively among smokers, ensemble machine learning methods have been used. A single model is created based on ensemble voting classifier with a combined boosting technique to improve the accuracy of predictive model. The final ensemble model shows an accuracy of 95.20%, precision of 97.27%, sensitivity of 92.35%, specificity of 98.07%, F1 score of 0.95, AUC of 0.961, MCE of 0.0479, kappa statistics value of 0.9041, and MSE of 0.2189. The obtained accuracy by using the proposed method is the highest value achieved so far for the prediction of CD among smokers using HRV data.
Keyword(s):
Keyword(s):
2020 ◽
Vol 1625
◽
pp. 012024
2019 ◽
Vol 9
(1)
◽
pp. 442-444
Keyword(s):
2021 ◽
Vol 23
(1)
◽
pp. 111-114