The prediction and analysis of heart disease using XGBoost algorithm

Research Article
Open access

The prediction and analysis of heart disease using XGBoost algorithm

Juan Carlos Yang 1*
  • 1 Auburn University    
  • *corresponding author jzy0103@auburn.edu
Published on 22 February 2024 | https://doi.org/10.54254/2755-2721/41/20230711
ACE Vol.41
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-307-4
ISBN (Online): 978-1-83558-308-1

Abstract

Heart diseases remain a global health concern, with their intricate aetiology and multifactorial risk factors making early diagnosis challenging. Recognizing the pressing need for accurate prediction tools, this research ventured into harnessing the power of machine learning, notably the Xtreme Gradient Boosting (XGBoost) algorithm, to fill this gap. The main object is to devise a robust predictive framework capable of early and accurate identification of heart disease. Specifically, our methodology unfolded systematically, beginning with data preprocessing, then delving into incisive feature selection, rigorous model training, and finally, thorough evaluation. This study is meticulously conducted on the ‘heart.csv’ dataset, a comprehensive repository of cardiovascular data points. The experimental outcomes were nothing short of revelatory. Not only did the XGBoost model manifest superior performance metrics, but its precision also outpaced several contemporary models referenced in existing literature. Ultimately, our findings underscore the profound potential of the XGBoost algorithm in heart disease predictions. Beyond academic intrigue, this research holds tangible implications for healthcare practitioners, potentially offering a novel tool for early interventions and patient management.

Keywords:

XGBoost, Heart Disease Prediction, Feature Selection

Yang,J.C. (2024). The prediction and analysis of heart disease using XGBoost algorithm . Applied and Computational Engineering,41,61-68.
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References

[1]. World Health Organization 2020 Cardiovascular diseases (CVDs) WHO https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-%28cvds%29

[2]. Shah D Patel S and Bharti S K 2020 Heart Disease Prediction using Machine Learning Techniques. SN COMPUT SCI 1: p 345

[3]. Alty S R Millasseau S C Chowienczyc P J and Jakobsson A 2003 Cardiovascular disease prediction using support vector machines 46th Midwest Symposium on Circuits and Systems 1: pp 376-379

[4]. Jones R 2021 Neural Networks in Cardiac Predictions Cardiology Today https://www.frontiersin.org/articles/10.3389/fphys.2021.734178/full

[5]. Doki S Devella S Tallam Reddy Gangannagari S S Sampathkrishna Reddy P and Reddy G P 2022 Heart Disease Prediction Using XGBoost Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT) : pp 1317-1320

[6]. Karadeniz T Tokdemir G and Maraş H H 2021 Ensemble Methods for Heart Disease Prediction New Gener Comput 39: pp 569–581

[7]. Chawla S Bowyer K Hall L O and Kegelmeyer W P 2002 SMOTE: Synthetic Minority Over-sampling Technique Journal of Artificial Intelligence Research 16: pp 321-357

[8]. Chen T and Guestrin C 2016 XGBoost: A Scalable Tree Boosting System Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining: pp 785-794

[9]. Bergstra J and Bengio Y 2012 Random Search for Hyper-Parameter Optimization Journal of Machine Learning Research 13: pp 281-305

[10]. Géron A 2020 Hands-On Machine Learning with Scikit-Learn and TensorFlow Concepts, Tools, and Techniques to Build Intelligent Systems 43: pp 11353-1136

[11]. Dataset https://www.kaggle.com/datasets/johnsmith88/heart-disease-dataset

Cite this article

Yang,J.C. (2024). The prediction and analysis of heart disease using XGBoost algorithm . Applied and Computational Engineering,41,61-68.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation

ISBN：978-1-83558-307-4(Print) / 978-1-83558-308-1(Online)
Editor：Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.41
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. World Health Organization 2020 Cardiovascular diseases (CVDs) WHO https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-%28cvds%29

[2]. Shah D Patel S and Bharti S K 2020 Heart Disease Prediction using Machine Learning Techniques. SN COMPUT SCI 1: p 345

[3]. Alty S R Millasseau S C Chowienczyc P J and Jakobsson A 2003 Cardiovascular disease prediction using support vector machines 46th Midwest Symposium on Circuits and Systems 1: pp 376-379

[4]. Jones R 2021 Neural Networks in Cardiac Predictions Cardiology Today https://www.frontiersin.org/articles/10.3389/fphys.2021.734178/full

[5]. Doki S Devella S Tallam Reddy Gangannagari S S Sampathkrishna Reddy P and Reddy G P 2022 Heart Disease Prediction Using XGBoost Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT) : pp 1317-1320

[6]. Karadeniz T Tokdemir G and Maraş H H 2021 Ensemble Methods for Heart Disease Prediction New Gener Comput 39: pp 569–581

[7]. Chawla S Bowyer K Hall L O and Kegelmeyer W P 2002 SMOTE: Synthetic Minority Over-sampling Technique Journal of Artificial Intelligence Research 16: pp 321-357

[8]. Chen T and Guestrin C 2016 XGBoost: A Scalable Tree Boosting System Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining: pp 785-794

[9]. Bergstra J and Bengio Y 2012 Random Search for Hyper-Parameter Optimization Journal of Machine Learning Research 13: pp 281-305

[10]. Géron A 2020 Hands-On Machine Learning with Scikit-Learn and TensorFlow Concepts, Tools, and Techniques to Build Intelligent Systems 43: pp 11353-1136

[11]. Dataset https://www.kaggle.com/datasets/johnsmith88/heart-disease-dataset

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