A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Classification of Photoplethysmography Signals using Ensemble Machine Learning
In this study, we proposed an ensemble model using neural networks and supervised learning classifiers to predict blood pressure, along with seven basic classifiers, i.e., CATBoost, XGBoost, Random Forest, Support Vector Machine, Decision Tree, K Nearest Neighbor, and Logistic Regression. Furthermore, evaluation metrics such as accuracy, F1 score, precision, and recall are calculated. Ensemble model using artificial neural networks and decision tree classifier gives an accuracy of 100% and an F1 score of 1.0. This study was able to predict. However, the purpose of this study is to assess the competence of features retrieved using photoplethysmography (PPG)
Classification of Photoplethysmography Signals using Ensemble Machine Learning
In this study, we proposed an ensemble model using neural networks and supervised learning classifiers to predict blood pressure, along with seven basic classifiers, i.e., CATBoost, XGBoost, Random Forest, Support Vector Machine, Decision Tree, K Nearest Neighbor, and Logistic Regression. Furthermore, evaluation metrics such as accuracy, F1 score, precision, and recall are calculated. Ensemble model using artificial neural networks and decision tree classifier gives an accuracy of 100% and an F1 score of 1.0. This study was able to predict. However, the purpose of this study is to assess the competence of features retrieved using photoplethysmography (PPG)
Classification of Photoplethysmography Signals using Ensemble Machine Learning
Nasir, Nida (author) / Sameer, Mustafa (author) / Alshaltone, Omar (author) / Barneih, Feras (author) / Al-Shabi, Mohammad (author) / Al-Shammaa, Ahmed (author)
2023-02-20
484853 byte
Conference paper
Electronic Resource
English