Diagnosis of coronary artery disease based on machine learning algorithms support vector machine, artificial neural network, and random forest

Authors
Saeed Saeedbakhsh 1
Mohammad Sattari 1
Maryam Mohammadi 2
Jamshid Najafian 3
Farzaneh Mohammadi 4
1 Health Information Technology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
2 Department of Management and Health Information Technology, School of Management and Medical Information Sciences, Isfahan University of Medical Sciences, Isfahan, Iran
3 Isfahan Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran
4 Department of Environmental Health Engineering, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran
Abstract
Background: Coronary artery disease (CAD) is known as the most common cardiovascular disease. The development of CAD is influenced by several risk factors. Diagnostic and therapeutic methods of this disease have many and costly side effects. Therefore, researchers are looking for cost-effective and accurate methods to diagnose this disease. Machine learning algorithms can help specialists diagnose the disease early. The aim of this study is to detect CAD using machine learning algorithms.
Materials and Methods: In this study, three data mining algorithms support vector machine (SVM), artificial neural network (ANN), and random forest were used to predict CAD using the Isfahan Cohort Study dataset of Isfahan Cardiovascular Research Center. 19 features with 11495 records from this dataset were used for this research.
Results: All three algorithms achieved relatively close results. However, the SVM had the highest accuracy compared to the other techniques. The accuracy was calculated as 89.73% for SVM. The ANN algorithm also obtained the high area under the curve, sensitivity and accuracy and provided acceptable performance. Age, sex, Sleep satisfaction, history of stroke, history of palpitations, and history of heart disease were most correlated with target class. Eleven rules were also extracted from this dataset with high confidence and support.
Conclusion: In this study, it was shown that machine learning algorithms can be used with high accuracy to detect CAD. Thus, it allows physicians to perform timely preventive treatment in patients with CAD.

Keywords

1.
Mastoi QU, Wah TY, Gopal Raj R, Iqbal U. Automated diagnosis of coronary artery disease: A review and workflow. Cardiol Res Pract 2018;2018:2016282.  Back to cited text no. 1
    
2.
Alizadehsani R, Khosravi A, Roshanzamir M, Abdar M, Sarrafzadegan N, Shafie D, et al. Coronary artery disease detection using artificial intelligence techniques: A survey of trends, geographical differences and diagnostic features 1991-2020. Comput Biol Med 2021;128:104095.1.  Back to cited text no. 2
    
3.
Alizadehsani R, Habibi J, Hosseini MJ, Mashayekhi H, Boghrati R, Ghandeharioun A, et al. A data mining approach for diagnosis of coronary artery disease. Comput Methods Programs Biomed 2013;111:52-61.  Back to cited text no. 3
    
4.
Hassannataj Joloudari J, Saadatfar H, Dehzangi A, Shamshirband S. Computer-aided decision-making for predicting liver disease using PSO-based optimized SVM with feature selection. Inform Med Unlocked 2019;17:100255.  Back to cited text no. 4
    
5.
Ajami S, Mohammadi M. Telemedicine against CoVID-19 crisis. Health Policy Technol 2020;9:277-8.  Back to cited text no. 5
    
6.
Amin MS, Chiam YK, Varathan KD. Identification of significant features and data mining techniques in predicting heart disease. Telemat Inform 2019;36:82-93.  Back to cited text no. 6
    
7.
Joloudari JH, Joloudari EH, Saadatfar H, GhasemiGol M, Razavi SM, Mosavi A, et al. Coronary artery disease diagnosis; ranking the significant features using a random trees model. Int J Environ Res Public Health 2020;17:731.  Back to cited text no. 7
    
8.
Libby P, Zipes D, Bonow R, Mann D, Tomaselli G. Braunwald's Heart Disease e-Book: A Textbook of Cardiovascular Medicine. 11th ed. Amsterdam, Netherlands: Elsevier Health Sciences; 2018.  Back to cited text no. 8
    
9.
Bagherian H, Haghjooy Javanmard S, Sharifi M, Sattari M. Using data mining techniques for predicting the survival rate of breast cancer patients: A review article. Tehran Univ Med J 2021;79:176-86.  Back to cited text no. 9
    
10.
Saeedbakhsh S, Sattari M, Mohammadi M, Najafian J. Application of data mining techniques in predicting coronary heart disease: A systematic review. Int J Environ Health Eng 2021;10:5. Available from: https://www.ijehe.org/article.asp?issn=2277-9183;year=2021;volume=10;issue=1;spage=5;epage=5;aulast=Saeedbakhsh. [Last accessed on 2022 Jan 29].  Back to cited text no. 10
    
11.
Shahid AH, Singh MP. A novel approach for coronary artery disease diagnosis using hybrid particle swarm optimization based emotional neural network. Biocybern Biomed Eng 2020;40:1568-85.  Back to cited text no. 11
    
12.
Jackins V, Vimal S, Kaliappan M, Lee MY. AI-based smart prediction of clinical disease using random forest classifier and Naive Bayes. J Supercomput 2021;77:5198-219. Available from: https://link.springer.com/article/10.1007/s11227-020-03481-x. [Last accessed on 2022 Jan 25].  Back to cited text no. 12
    
13.
Das R, Turkoglu I, Sengur A. Effective diagnosis of heart disease through neural networks ensembles. Expert Syst Appl 2009;36:7675-80.  Back to cited text no. 13
    
14.
Olaniyi EO, Oyedotun OK, Adnan K. Heart diseases diagnosis using neural networks arbitration. Int J Intell Syst Appl 2015;7:72.  Back to cited text no. 14
    
15.
Dutta A, Batabyal T, Basu M, Acton ST. An efficient convolutional neural network for coronary heart disease prediction. Expert Syst Appl 2020;159:113408.  Back to cited text no. 15
    
16.
Mazaheri S, Ashoori M, Bechari Z. A model to predict heart disease treatment using data mining. Payavard 2017;11:287-96. Available from: http://payavard.tums.ac.ir/article-1-6285-en.html. [Last accessed on 2022 Jan 26].  Back to cited text no. 16
    
17.
Sh S, Tolooie HR. Isfahan Healthy Heart Program: A comprehensive integrated community-based program for cardiovascular disease prevention and control design, methods and initial experience, 2000-01. J Res Med Sci 2002;1:1-8.  Back to cited text no. 17
    
18.
Al Jarullah AA. Decision tree discovery for the diagnosis of type II diabetes. In: 2011 International conference on innovations in information technology. IEEE; 2011. p. 303-7.  Back to cited text no. 18
    
19.
Rodríguez D, Sicilia MA, García E, Harrison R. Empirical findings on team size and productivity in software development. J Syst Softw 2011;85:562-70. Available from: http://www.pmi.org/. [Last accessed on 2021 Dec 21].  Back to cited text no. 19
    
20.
Mohammadi F, Pourzamani H, Karimi H, Mohammadi M, Mohammadi M, Ardalan N, et al. Artificial neural network and logistic regression modelling to characterize COVID-19 infected patients in local areas of Iran. Biomed J 2021;44:304-16.  Back to cited text no. 20
    
21.
Akella A, Akella S. Machine learning algorithms for predicting coronary artery disease: Efforts toward an open source solution. Future Sci OA 2021;7:FSO698.  Back to cited text no. 21
    
22.
IRMA. Fuzzy Systems: Concepts, Methodologies, Tools, and Applications. Vols. 1-3. Hershey, Pennsylvania: IGI Global; 2017. p. 1-1765.  Back to cited text no. 22
    
23.
Ali F, El-Sappagh S, Islam SM, Kwak D, Ali A, Imran M, et al. A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion. Inf Fusion 2020;63:208-22.  Back to cited text no. 23
    
24.
Md Idris N, Chiam YK, Varathan KD, Wan Ahmad WA, Chee KH, Liew YM. Feature selection and risk prediction for patients with coronary artery disease using data mining. Med Biol Eng Comput 2020;58:3123-40.  Back to cited text no. 24
    
25.
Galdi P, Tagliaferri R. Data mining: Accuracy and error measures for classification and prediction. In: Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics. Amsterdam, Netherlands: Elsevier; 2018. p. 431-6.  Back to cited text no. 25
    
26.
Tong Z, Liu Y, Ma H, Zhang J, Lin B, Bao X, et al. Development, validation and comparison of artificial neural network models and logistic regression models predicting survival of unresectable pancreatic cancer. Front Bioeng Biotechnol 2020;8:196.  Back to cited text no. 26
    
27.
Hahsler M, Grün B, Hornik K. Arules – A computational environment for mining association rules and frequent item sets. J Stat Softw 2005;14:1-25.  Back to cited text no. 27
    
28.
Saltelli A, Andres TH, Homma T. Sensitivity analysis of model output. An investigation of new techniques. Comput Stat Data Anal 1993;15:211-38.  Back to cited text no. 28
    
29.
Alizadehsani R, Habibi J, Hosseini MJ, Boghrati R, Ghandeharioun A, Bahadorian B, et al. Diagnosis of coronary artery disease using data mining techniques based on symptoms and ECG features. Eur J Sci Res 2012;82:542-53.  Back to cited text no. 29
    
30.
Desai U, Martis RJ, Nayak CG, Sarika K, Seshikala G. Machine Intelligent Diagnosis of ECG for Arrhythmia Classification Using DWT, ICA and SVM Techniques. 12th IEEE International Conference Electronics, Energy, Environment, Communication, Computer, Control: (E3-C3), INDICON 2015; 2016. p. 2-5.  Back to cited text no. 30
    
31.
Ekiz S, Erdogmus P. Comparative Study of Heart Disease Classification. 2017 Electric Electronics. Computer Science, Biomedical Engineerings' Meeting, EBBT 2017; 2017. p. 1-4.  Back to cited text no. 31
    
32.
Byun H, Lee SW. A survey on pattern recognition applications of support vector machines. Int J Pattern Recognit Artif Intell 2003;17:459-86.  Back to cited text no. 32
    
33.
Huang MW, Chen CW, Lin WC, Ke SW, Tsai CF. SVM and SVM ensembles in breast cancer prediction. PLoS One 2017;12:e0161501.  Back to cited text no. 33
    
34.
Adewumi AA, Owolabi TO, Alade IO, Olatunji SO. Estimation of physical, mechanical and hydrological properties of permeable concrete using computational intelligence approach. Appl Soft Comput 2016;42:342-50.  Back to cited text no. 34
    
35.
Yerushalmy J. Statistical problems in assessing methods of medical diagnosis, with special reference to X-ray techniques. Public Health Rep 1947;62:1432-49.  Back to cited text no. 35
    
36.
Brereton RG, Lloyd GR. Support vector machines for classification and regression. Analyst 2010;135:230-67.  Back to cited text no. 36
    
37.
Prasad A, Edward JB. Importance of Artificial Neural Networks for Location of Faults in Transmission Systems: A Survey. In: 2017 11th International Conference on Intelligent Systems and Control (ISCO). IEEE; 2017. p. 357-62.  Back to cited text no. 37
    
38.
Dipto IC, Islam T, Rahman HM, Rahman MA, Dipto IC, Islam T, et al. Comparison of different machine learning algorithms for the prediction of coronary artery disease. J Data Anal Inf Process 2020;8:41-68. Available from: http://www.scirp.org/journal/PaperInformation.aspx?PaperID=99402. [Last accessed on 2021 Sep 07].  Back to cited text no. 38
    
39.
Abrunhosa Collazo R, Antonio Monteiro Pessôa L, Bahiense L, de Bragança Pereira B, Faria dos Reis A, Souza Silva N. A comparative study between artificial neural network and support vector machine for acute coronary syndrome prognosis. Pesqui Oper 2016;36:321-43. Available from: http://www.scielo.br/pope. [Last accessed on 2021 Sep 07].  Back to cited text no. 39
    
40.
Almansour NA, Syed HF, Khayat NR, Altheeb RK, Juri RE, Alhiyafi J, et al. Neural network and support vector machine for the prediction of chronic kidney disease: A comparative study. Comput Biol Med 2019;109:101-11.  Back to cited text no. 40
    
41.
Gudadhe M, Wankhade K, Dongre S. Decision Support System for Heart Disease Based on Support Vector Machine and Artificial Neural Network. 2010 International Conference on Computer and Communication Technology ICCCT-2010; 2010. p. 741-5.  Back to cited text no. 41
    
42.
Arnaout MS, Almahmeed W, Ibrahim M, Ker J, Khalil MT, Van Wyk CT, et al. Hypertension and its management in countries in Africa and the Middle East, with special reference to the place of β-blockade. Curr Med Res Opin 2011;27:1223-36.  Back to cited text no. 42
    
43.
Hajar R. Risk factors for coronary artery disease: Historical perspectives. Heart Views 2017;18:109. Available from: http://pmc/articles/PMC5686931/. [Last accessed on 2021 Sep 05].  Back to cited text no. 43
    
44.
Al-Nozha M, Al-Khadra A, Arafah MR, Al-Maatouq MA, Khalil MZ, Khan NB, et al. Metabolic syndrome in Saudi Arabia. Saudi Med J 2005;26:1918-25. Available from: https://europepmc.org/article/med/16380773. [Last accessed on 2021 Sep 05].  Back to cited text no. 44
    
45.
Gotto AM Jr. Triglyceride as a risk factor for coronary artery disease. Am J Cardiol 1998;82:22Q-5Q.  Back to cited text no. 45
    
46.
Lao XQ, Liu X, Deng HB, Chan TC, Ho KF, Wang F, et al. Sleep quality, sleep duration, and the risk of coronary heart disease: A prospective cohort study with 60,586 adults. J Clin Sleep Med 2018;14:109-17.  Back to cited text no. 46
Advanced Biomedical Research
Volume 2023, February
February 2023
Pages 1-8