Estimation of Risk Factors Related to Heart Attack with Xgboost That Machine Learning Model

Year 2022, Volume: 8 Issue: 4, 582 - 591, 30.11.2022

Onural Özhan , Zeynep Küçükakçalı

https://doi.org/10.19127/mbsjohs.1142542

Cited By: 2

Abstract

Objective: The objective of this work is to classify heart attack cases using the open-access heart attack dataset and one of the machine learning techniques called XGBoost. Another aim is to reveal the risk factors associated with having a heart attack as a result of the modeling and to associate these factors with heart attack.
Methods: In the study, modeling was done with the XGBoost method using an open access data set including the factors associated with heart attack. Model results were evaluated with accuracy, balanced accuracy, specificity, positive predictive value, negative predictive value, and F1-score performance metrics. In addition, 10-fold cross-validation method was used in the modeling phase. Finally, variable importance values were obtained by modeling.
Results: Accuracy, balanced accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score from by XGBoost modeling were 89.4%, 89.4%, 88.4%, 90.3%, 88.4%, 90.3%, and 88.4%, respectively. According to the variable importance values obtained for the input variables in the data set examined in this study, thal2, oldpeak, thal3, ca1, and exang1 were obtained as the most important variables associated with heart attack.
Conclusions: With the machine learning model used, the heart attack dataset was classified quite successfully, and the associated risk factors were revealed. Machine learning models can be used as clinical decision support systems for early diagnosis and treatment.

Keywords

Heart attack, machine learning, XGBoost, modelling, variable importance

References

• 1- Abanonu G. Major risk factors for coronary artery disease and evaluation of C-Reactive protein. Published Specialization Thesis Istanbul. 2005.
• 2- House W. Follow-up to the political declaration of the high-level meeting of the general assembly on the prevention and control of non-communicable diseases. World Health Organization. 2013.
• 3- Lee CH, Kim J-H. A review on the medicinal potentials of ginseng and ginsenosides on cardiovascular diseases. J Ginseng Res. 2014;38(3):161-6.
• 4- Halıcı Z, Yasin Bayır HS, Çadırcı E, Keleş MS, Bayram E. Investigation of the Effects of Amiodarone on Erythropoietin Levels in Isoproterenol-induced Acute and Chronic Myocardial Infarction Model in Rats. The Eurasian Journal of Medicine. 2002;38:68-72
• 5- Storrow AB, Gibler WB. Chest pain centers: diagnosis of acute coronary syndromes. Ann Emerg Med. 2000;35(5):449-61.
• 6- Şentürk S. Investigation of the effect of l-lysine on total sialic acid levels in rats with myocardial infarction with isoproterenol. Trakya University Institute of Health Sciences Department of Biochemistry Master's Program Erzurum, 2008.
• 7- Polikar R. Ensemble learning. Ensemble machine learning: Springer; 2012. p. 1-34.
• 8- Akman M, Genç Y, Ankarali H. Random Forests Yöntemi ve Saglik Alaninda Bir Uygulama/Random Forests Methods and an Application in Health Science. Turkey Clinics Biostatistics. 2011;3(1):36.
• 9- Witten IH, Frank E. Data mining: practical machine learning tools and techniques with Java implementations. Acm Sigmod Record. 2002;31(1):76-7.
• 10- Dikker J. Boosted tree learning for balanced item recommendation in online retail. Master thesis. 2017.
• 11- Patrous ZS. Evaluating XGBoost For User Classification by Using Behavioral Features Extracted from Smartphone Sensors. [Master Thesis]: KTH Royal Institute of Technology, School of Computer Science and Communication, Sweden.; 2018. Access link: https://www.diva-portal.org/smash/get/diva2:1240595/FULLTEXT01.pdf
• 12- Wang J, Li P, Ran R, Che Y, Zhou Y. A short-term photovoltaic power prediction model based on the gradient boost decision tree. Appl Sci. 2018;8(5):689.
• 13- Ogunleye A, Wang Q-G. XGBoost model for chronic kidney disease diagnosis. IEEE/ACM Trans Comput Biol Bioinform. 2019;17(6):2131-40.
• 14- Li W, Yin Y, Quan X, Zhang H. Gene expression value prediction based on XGBoost algorithm. Frontiers in Genetics. 2019;10:1077.
• 15- Organization WH. Hearts: technical package for cardiovascular disease management in primary health care. 2016. Access link: https://apps.who.int/iris/bitstream/handle/10665/252661/9789241511377-eng.pdf
• 16- Thippeswamy B, Thakker S, Tubachi S, Kalyani G, Netra M, Patil U, et al. Cardioprotective effect of Cucumis trigonus Roxb on isoproterenol-induced myocardial infarction in rat. Am J Pharmacol Toxicol 2009;4(2):29-37.
• 17- Ateş S. Determining the Most Appropriate Ambulance Locations for Heart Attack Cases with Geographic Information Systems: Graduate School of Sciences; 2010.
• 18- Upaganlawar A, Gandhi H, Balaraman R. Isoproterenol induced myocardial infarction: protective role of natural products. J Pharmacol Toxicol. 2011;6(1):1-17.
• 19- Alpaydin E. Introduction to machine learning: MIT press; 2020.
• 20- Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J. 2019;6(2):94.
• 21- Yu K-H, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat. Biomed. Eng. 2018;2(10):719-31.
• 22- Zeynep T, İpek BC, Guldogan E. Performance evaluation of the deep learning models in the classification of heart attack and determination of related factors. J. Cogn. Sci. 2020;5(2):99-103.