Abstract
The chapter explores the supervised machine learning techniques used in different investigations to predict whether a patient will have a heart failure or not. The research focuses on the articles that used the “Cleveland Heart Disease Data Set” from the UCI Machine Learning Repository and performs a comparison of the different techniques used to find the best performance. A conclusion of the best technique is also provided in this chapter. Some examples of the techniques are C4.5 tree, Naïve Bayes, Bayesian Neural Networks (BNN), Support Vector Machine (SVM), Artificial Neural Network (ANN), Nearest Neighbor (KNN).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
WHO Homepage (2018), http://www.who.int/cardiovascular_diseases/en/. Last Accessed 19 June 2018
HEART Homepage (2018), http://www.heart.org/HEARTORG/Conditions/HeartFailure/Heart-Failure_UCM_002019_SubHomePage.jsp. Last Accessed 19 June 2018
S. Shalev-Shwartz, S. Ben-David, Understanding Machine Learning: From Theory to Algorithms (Cambridge University Press, New York, 2016)
M.M. Al Rahhal et al., Deep learning approach for active classification of electrocardiogram signals. Inf. Sci. 345, 340–354 (2016)
A.F. Khalaf, M.I. Owis, I.A. Yassine, A novel technique for cardiac arrhythmia classification using spectral correlation and support vector machines. Expert Syst. Appl. 42(21), 8361–8368 (2015)
G. Guidi, M.C. Pettenati, P. Melillo, E. Iadanza, A machine learning system to improve heart failure patient assistance. IEEE J. Biomed. Health Inform. 18(6), 1750–1756 (2014)
G. Parthiban, S.K. Srivatsa, Applying machine learning methods in diagnosing heart disease for diabetic patients. Int. J. Appl. Inf. Syst. 3(7), 25–33 (2012)
UCI Heart Disease Data Set (2018), http://archive.ics.uci.edu/ml/datasets/heart+disease. Last Accessed 20 June 2018
S. Marsland, Machine Learning: An Algorithmic Perspective (Chapman and Hall/CRC, 2015)
M. Bramer, Principles of Data Mining (Springer London Ltd., 2013)
E. Frank, M.A. Hall, I.H. Witten, The WEKA Workbench. Online Appendix for “Data Mining: Practical Machine Learning Tools and Techniques”, 4th edn. (Morgan Kaufmann, 2016)
H. Trevor et al., The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer, 2017)
D. Khanna et al., Comparative study of classification techniques (SVM, logistic regression and neural networks) to predict the prevalence of heart disease. Int. J. Mach. Learn. Comput. 5(5), 414–419 (2015)
J.R. Quinlan, Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)
C.E. Shannon, A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423, 623–656 (1948)
J.R. Quinlan, C4.5: Programs for Machine Learning (Morgan Kaufmann Publishers, 1993)
S.S. Khan, Prediction of angiographic disease status using rule based data mining techniques. Biol. Forum Int. J. 8(2), 103–107 (2016)
L. Breiman, Random Forest, vol. 45 (Kluwer Academic Publishers, 2001), pp. 5–32
S. Kodati, Analysis of heart disease using in data mining tools Orange and Weka. Glob. J. Comput. Sci. Technol. 18–1 (2018)
N. Mutyala, Prediction of heart diseases using data mining and machine learning algorithms and tools. Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol. 3–3 (2018)
M. Shouman et al., Applying k-nearest neighbour in diagnosing heart disease patients. Int. J. Inf. Educ. Technol., 220–223 (2012)
M. Wadhonkar, A data mining approach for classification of heart disease dataset using neural network. Int. J. Appl. Innov. Eng. Manag. 4(5), 426–433 (2015)
V.N. Vapnik, The Nature of Statistical Learning Theory (Springer, New York, NY, USA, 1995)
K. Srinivas et al., Analysis of coronary heart disease and prediction of heart attack in coal mining regions using data mining techniques, in 2010 5th International Conference on Computer Science & Education (2010)
OneR (2018), http://www.saedsayad.com/oner.htm. Last Accessed 19 June 2018
ZeroR (2018), http://chem-eng.utoronto.ca/~datamining/dmc/zeror.htm. Last Accessed 19 June 2018
A.Q. Ansari, N.K. Gupta, Automated diagnosis of coronary heart disease using neuro-fuzzy integrated system, in 2011 World Congress on Information and Communication Technologies (2011)
S. Mokeddem et al., Supervised feature selection for diagnosis of coronary artery disease based on genetic algorithm, in Computer Science & Information Technology (CS & IT) (2013)
N. Ziasabounchi, I. Askerzarde, ANFIS based classification model for heart disease prediction. Int. J. Electr. Comput. Sci. 14(2), 7–12 (2014)
J.-S.R. Jang, ANFIS: adaptive-network-based fuzzy inference system
T. Vivekanandan, N.C.S.N. Iyengar, Optimal feature selection using a modified differential evolution algorithm and its effectiveness for prediction of heart disease. Comput. Biol. Med. 90, 125–136 (2017)
M. Mitchell, An Introduction to Genetic Algorithms (MIT Press, Cambridge, MA, 1996)
N.G.B. Amma, Cardiovascular disease prediction system using genetic algorithm and neural network, in 2012 International Conference on Computing, Communication and Applications (2012)
K. Uyar, A. Ilhan, Diagnosis of heart disease using genetic algorithm based trained recurrent fuzzy neural networks. Procedia Comput. Sci. 120, 588–593 (2017)
L. Parthiban, Intelligent heart disease prediction system using CANFIS and genetic algorithm. Int. J. Biol. Med. Sci. 3(3), 157–160 (2008)
A. Acharya, Comparative Study of Machine Learning Algorithms for Heart Disease Prediction (Helsinki Metropolia University of Applied Sciences, 2017)
T. Santhanam, E.P. Ephzibah, Heart disease classification using PCA and feed forward neural networks. Min. Intell. Knowl. Explor. Lect. Notes Comput. Sci., 90–99 (2013)
J. Soni, Intelligent and effective heart disease prediction system using weighted associative classifiers. Int. J. Comput. Sci. Eng. 5(6), 2385–2392 (2011)
D. Chaki et al., A comparison of three discrete methods for classification of heart disease data. Bangladesh J. Sci. Ind. Res. 50(4), 293 (2015)
S.K. Sen, Predicting and diagnosing of heart disease using machine learning algorithms. Int. J. Eng. Comput. Sci. (2017)
A. Olsson, D. Nordlof, Early Screening Diagnostic Aid for Heart Disease Using Data Mining (2015)
M. Shouman, T. Turner, R. Stocker, Using decision tree for diagnosing heart disease patients, in 9th Australasian Data Mining Conference, vol. 121, pp. 23–30
R. Das et al., Effective diagnosis of heart disease through neural networks ensembles. Expert. Syst. Appl. 36(4), 7675–7680 (2009)
A. Caliskan, M.E. Yuksel, Classification of coronary artery disease data sets by using a deep neural network. EuroBiotech J. 1(4), 271–277 (2017)
M.A.M. Abushariah et al., Automatic heart disease diagnosis system based on artificial neural network (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) approaches. J. Softw. Eng. Appl. 07(12), 1055–1064 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Garate Escamilla, A.K., Hajjam El Hassani, A., Andres, E. (2019). A Comparison of Machine Learning Techniques to Predict the Risk of Heart Failure. In: Tsihrintzis, G., Virvou, M., Sakkopoulos, E., Jain, L. (eds) Machine Learning Paradigms. Learning and Analytics in Intelligent Systems, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-030-15628-2_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-15628-2_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-15627-5
Online ISBN: 978-3-030-15628-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)