Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Prediction of heart disease using XGB classifier Description An account of the resource 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 An entity primarily responsible for making the resource Vijayalakshmi S.; Sivakumar V.; Nataraj C.; Kanth P.C. Source A related resource from which the described resource is derived AIP Conference Proceedings, Vol-3161, No. 1 Publisher An entity responsible for making the resource available American Institute of Physics Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1063/5.0229493" target="_blank" rel="noreferrer noopener">https://doi.org/10.1063/5.0229493</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203980484&amp;doi=10.1063%2F5.0229493&amp;partnerID=40&amp;md5=693fe65e4eb2ab89af2338d538b95d93" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203980484&amp;doi=10.1063%2f5.0229493&amp;partnerID=40&amp;md5=693fe65e4eb2ab89af2338d538b95d93</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 0094243X Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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