Version 1
: Received: 24 July 2024 / Approved: 24 July 2024 / Online: 25 July 2024 (12:57:22 CEST)
How to cite:
Palanisamy, S.; Rajaguru, H. Leveraging Classifier Performance Using Heuristic Optimization for Detecting Cardiovascular Disease from PPG Signals. Preprints2024, 2024071991. https://doi.org/10.20944/preprints202407.1991.v1
Palanisamy, S.; Rajaguru, H. Leveraging Classifier Performance Using Heuristic Optimization for Detecting Cardiovascular Disease from PPG Signals. Preprints 2024, 2024071991. https://doi.org/10.20944/preprints202407.1991.v1
Palanisamy, S.; Rajaguru, H. Leveraging Classifier Performance Using Heuristic Optimization for Detecting Cardiovascular Disease from PPG Signals. Preprints2024, 2024071991. https://doi.org/10.20944/preprints202407.1991.v1
APA Style
Palanisamy, S., & Rajaguru, H. (2024). Leveraging Classifier Performance Using Heuristic Optimization for Detecting Cardiovascular Disease from PPG Signals. Preprints. https://doi.org/10.20944/preprints202407.1991.v1
Chicago/Turabian Style
Palanisamy, S. and Harikumar Rajaguru. 2024 "Leveraging Classifier Performance Using Heuristic Optimization for Detecting Cardiovascular Disease from PPG Signals" Preprints. https://doi.org/10.20944/preprints202407.1991.v1
Abstract
Photoplethysmography (PPG) signals, which measure blood volume changes through light absorption, are increasingly used for non-invasive Cardiovascular Disease (CVD) detection. Analyzing PPG signals can help identify irregular heart patterns and other indicators of CVD. This research involves a thorough analysis of CVD classification using the Capnobase dataset, which includes data from 20 CVD subjects and 21 normal subjects. In the initial stage, heuristic optimization algorithms, such as ABC-PSO, the Cuckoo Search algorithm (CSA), and the Dragonfly algorithm (DFA), were applied to reduce the dimension of the PPG data. Next, these Dimensionally Reduced (DR) PPG data are then fed into various classifiers such as Linear Regression(LR), Linear Regression with Bayesian Linear Discriminant Classifier (LR-BLDC), K-Nearest Neighbors (KNN), PCA-Firefly, Linear Discriminant Analysis (LDA), Kernel LDA (KLDA), Probabilistic LDA (ProbLDA), SVM-Linear, SVM-Polynomial, SVM-RBF, to identify CVD. Classifier performance is evaluated using Accuracy, Kappa, MCC, F1 Score, Good Detection Rate(GDR), Error rate, and Jaccard Index(JI). The SVM-RBF classifier for ABC PSO dimensionality reduced values outperforms other classifiers, achieving an accuracy of 95.12% with a MCC of 0.90.
Keywords
CVD, Dimensionality Reduction, LR, KNN, LDA, SVM
Subject
Engineering, Bioengineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
