Prediction of heart disease using XGB classifier
description: Predicting heart disease in advance could be a significant medical breakthrough because it is widespread. A reliable strategy that can be utilized to do this is machine learning. Decision tree classifiers, random forests, and multilayer perceptron have all been used in studies to predict heart disease. However, several of these techniques could be improved, like poor precision. In our research, we have taken the South African heart Disease dataset and implemented a few models, which include Support Vector Machine (SVM), K Neighbors (KNN), Artificial neural network and XG Boost Classifier. We have used different methods for measuring performance. SVM with 69.0 accuracy, KNN with 86.0 accuracy, and ANN with 80.0 accuracy. However, the XGB classifier has shown some promising results in predicting heart disease with an accuracy of 90%. Further, when the hyperparameters were tuned using the random search method, the accuracy increased to 92.8%. The benefit of this work is that it uses machine-learning approaches to enhance the performance of coronary heart disease prediction. 2024 Author(s).
creator: Vijayalakshmi S.; Sivakumar V.; Nataraj C.; Kanth P.C.
source: AIP Conference Proceedings, Vol-3161, No. 1
publisher: American Institute of Physics
date: 2024-01-01
rights: Restricted Access
relation: ISSN: 0094243X
format: Online
language: English
type: Conference paper
coverage: Vijayalakshmi S., Department of Data Science, Christ University, Pune, India; Sivakumar V., School of Computing, Asia Pacific University of Technology and Innovation (APU), Kuala Lumpur, Malaysia; Nataraj C., School of Engineering, Asia Pacific University of Technology and Innovation (APU), Kuala Lumpur, Malaysia; Kanth P.C., Department of Data Science, Christ University, Pune, India