Cardiovascular disease prediction utilizing machine learning algorithm using grey wolf optimization method

In this paper, the authors suggest a unique method for increasing the precision of cardiovascular disease (CVD) prediction by combining gray wolf optimization (GWO) and machine learning techniques. The study makes use of a patient dataset to train and evaluate several machine learning techniques, su...

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Bibliographic Details
Main Authors: Chauhan, Navdeep Kaur, Kaur, Navdeep
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Algorithms
Decision trees
Heart diseases
Machine learning
Optimization
Support vector machines
Online Access:Get full text
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Summary:In this paper, the authors suggest a unique method for increasing the precision of cardiovascular disease (CVD) prediction by combining gray wolf optimization (GWO) and machine learning techniques. The study makes use of a patient dataset to train and evaluate several machine learning techniques, such as support vector machine (SVM), decision tree (DT), and k-nearest neighbor (k-NN) models. These models’ hyperparameters, such as the choice of features and parameter tweaking, are optimized using GWO. The findings demonstrate that the GWO-based machine learning model outperforms conventional models in terms of CVD prediction accuracy. The overall accuracy for SVM, Random Forest, and k-NN was 78%, 76%, and 56%, respectively, whereas the GWO’s accuracy was 87% in identifying the most crucial factors for CVD prediction.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0228691