Machine learning classification models for the patients who have heart failure

Year 2024, Volume: 42 Issue: 1, 235 - 244, 27.02.2024

Şevval Tuğçe Badik Mutlu Akar

Abstract

Heart failure is a cardiovascular disease with significant morbidity and mortality, affecting a growing number of people worldwide [1]. The aim of this paper is to predict the probability of survival of patients by looking at their various characteristics, diseases, and lifestyles in the most successful way by using various machine learning methods. The 299 patients in the data set we use, had left ventricular systolic dysfunction in 2015 and are classified as New York Heart Association (NYHA) class III and IV. The probability of survival of patients is estimated by applying various machine learning methods on the data set. In this study, there are two versions. In the first version of the study, Principal Component Analysis (PCA) is used to reduce the size of the data set. The performance of the machine learning algorithms is then evaluated using a variety of metrics. In the second version, the data set is only subjected to machine learning techniques, and performance is then assessed. Accuracy, Matthews correlation coefficient (MCC), sensitivity, specifity, F1 score, receiver operating characteristic-area under the curve (ROC-AUC), and precision-recall area under the curve (PR-AUC) values are calculated to measure success. Comparing the two versions reveals that all machine learning algorithms in general have performed better in the second version without PCA. In the second version, the CatBoost algorithm gave the most successful result. Patients with heart failure can have their mortality status predicted using machine learning techniques. The goal of this paper is to look at a variety of characteristics in order to assess the patient’s mortality status. The condition of the patient can be improved by selecting the proper treatment based on the mortality situation.

Keywords

Heart Failure, Machine Learning, Classification Algorithms, Principal Component Analysis, CatBoost

References

• REFERENCES
• [1] Morris JH, Chen L. Exercise training and heart failure: A review of the literature. Card Fail Rev 2019;5:5761. [CrossRef]
• [2] Bui AL, Horwich TB, Fonarow GC. Epidemiology and risk profile of heart failure. Nat Rev Cardiol. 2011;8:3041. [CrossRef]
• [3] McMurray JJ, Adamopoulos S, Anker SD, Auricchio A, Böhm M, Dickstein K, et al. ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure 2012: The Task Force for the Diagnosis and Treatment of Acute and Chronic Heart Failure 2012 of the European Society of Cardiology. Eur Heart J 2012;33:17871847.
• [4] Gong FF, Jelinek MV, Castro JM, Coller JM, McGrady M, Boffa U, et al. Risk factors for incident heart failure with preserved or reduced ejection fraction, and valvular heart failure, in a community-based cohort. Open Heart 2018;5:e000782. [CrossRef]
• [5] Ahmad T, Munir A, Bhatti SH, Aftab M, Raza MA. Survival analysis of heart failure patients: a case study. PLoS One 2017;12:e0181001. [CrossRef]
• [6] Zahid FM, Ramzan S, Faisal S, Hussain I. Gender-based survival prediction models for heart failure patients: a case study in Pakistan. PLoS One 2019;14:e0210602. [CrossRef]
• [7] Chicco D, Jurman G. Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone. BMC Med Inform Decis Mak 2020;20:16. [CrossRef]
• [8] Aujla RS, Patel R. Creatine Phosphokinase. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2021.
• [9] Gregg D, Goldschmidt-Clermont PJ. Cardiology patient page. Platelets and cardiovascular disease. Circulation 2003;108:e88e90. [CrossRef]
• [10] Mojadidi MK, Galeas JN, Goodman-Meza D, Eshtehardi P, Msaouel P, Kelesidis I, et al. Thrombocytopaenia as a prognostic indicator in heart failure with reduced ejection fraction. Heart Lung Circ 2016;25:568575. [CrossRef]
• [11] Metra M, Cotter G, Gheorghiade M, Dei Cas L, Voors AA. The role of the kidney in heart failure. Eur Heart J 2012;33:21352142. [CrossRef]
• [12] Bamira D, Picard MH. Imaging: Echocardiology-Assessment of Cardiac Structure and Function. In: Encyclopedia of Cardiovascular Research and Medicine. 2018. p. 3554. [CrossRef]
• [13] Patel Y, Joseph J. Sodium intake and heart failure. Int J Mol Sci 2020;21:9474. [CrossRef]
• [14] Bergstra J, Bengio Y. Random search for hyper-parameter optimization. J Mach Learn Res 2012;13:281305.
• [15] Akar M, Sirakov NM. Support vector machine skin lesion classification in Clifford algebra subspaces. Appl Math 2019;64:581598. [CrossRef]
• [16] Akar M, Sirakov NM, Mete M. Clifford algebra multivectors and kernels for melanoma classification. Math Methods Appl Sci 2022;45:40564068. [CrossRef]
• [17] Wold S, Esbensen K, Geladi P. Principal component analysis. Chemom Intell Lab Syst 1987;2:3752. [CrossRef]
• [18] Park HA. An introduction to logistic regression: from basic concepts to interpretation with particular attention to nursing domain. J Korean Acad Nurs 2013;43:154164. [CrossRef]
• [19] Rish I. An empirical study of the Naive Bayes classifier. In: IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence. 2001. p. 4146.
• [20] Cover T, Hart P. Nearest neighbor pattern classification. IEEE Trans Inform Theory 1967;13:2127. [CrossRef]
• [21] Durgesh S, Bhambhu L. Data classification using support vector machine. J Theor Appl Inf Technol 2010;12:17.
• [22] Shiruru K. An introduction to artificial neural networks. Int J Adv Res Innov Ideas Educ 2016;1:2730.
• [23] Song YY, Lu Y. Decision tree methods: applications for classification and prediction. Shanghai Arch Psychiatry 2015;27:130135.
• [24] Kulkarni V, Sinha P. Random forest classifiers: A survey and future research directions. Int J Adv Comput 2013;36:11441153.
• [25] Natekin A, Knoll A. Gradient boosting machines, a tutorial. Front Neurorobot 2013;7:21. [CrossRef]
• [26] Chen T, Guestrin C. XgBoost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2016 Aug 13-17; San Francisco, CA. New York: ACM; 2016. p. 785794. [CrossRef]
• [27] Wang Y, Guo R, Huang L, Yang S, Hu X, He K. m6AGE: A predictor for N6-methyladenosine sites identification utilizing sequence characteristics and graph embedding-based geometrical information. Front Genet 2021;12:670852. [CrossRef]
• [28] Benesty J, Chen J, Huang Y, Cohen I. Pearson correlation coefficient. In: Noise Reduction in Speech Processing. Springer Topics in Signal Processing Vol 2. Heidelberg: Springer; 2009. p. 14. [CrossRef]
• [29] Matthews BW. Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim Biophys Acta 1975;405:442451. [CrossRef]
• [30] Heo S, Lennie TA, Okoli C, Moser DK. Quality of life in patients with heart failure: ask the patients. Heart Lung 2009;38:100108. [CrossRef]
• [31] Çavuşoğlu Y, Zoghi M, Eren M, Bozçalı E, Kozdağ G, Şentürk T, et al. Post-discharge heart failure monitoring program in Turkey: Hit-PoinT. Anatol J Cardiol 2017;17:107112. [CrossRef]
• [32] Maayah B, Abu Arqub O, Alnabulsi S, Alsulami H. Numerical solutions and geometric attractors of a fractional model of the cancer-immune based on the Atangana-Baleanu-Caputo derivative and the reproducing kernel scheme. Chin J Phys 2022;80:463483. [CrossRef]