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BMC Public Health
Published in: BMC Public Health 1/2019

Open Access 01-12-2019 | Arterial Diseases | Research article

Predicting coronary artery disease: a comparison between two data mining algorithms

Authors: Haleh Ayatollahi, Leila Gholamhosseini, Masoud Salehi

Published in: BMC Public Health | Issue 1/2019

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Abstract

Background

Cardiovascular diseases (CADs) are the first leading cause of death across the world. World Health Organization has estimated that morality rate caused by heart diseases will mount to 23 million cases by 2030. Hence, the use of data mining algorithms could be useful in predicting coronary artery diseases. Therefore, the present study aimed to compare the positive predictive value (PPV) of CAD using artificial neural network (ANN) and SVM algorithms and their distinction in terms of predicting CAD in the selected hospitals.

Methods

The present study was conducted by using data mining techniques. The research sample was the medical records of the patients with coronary artery disease who were hospitalized in three hospitals affiliated to AJA University of Medical Sciences between March 2016 and March 2017 (n = 1324). The dataset and the predicting variables used in this study was the same for both data mining techniques. Totally, 25 variables affecting CAD were selected and related data were extracted. After normalizing and cleaning the data, they were entered into SPSS (V23.0) and Excel 2013. Then, R 3.3.2 was used for statistical computing.

Results

The SVM model had lower MAPE (112.03), higher Hosmer-Lemeshow test’s result (16.71), and higher sensitivity (92.23). Moreover, variables affecting CAD (74.42) yielded better goodness of fit in SVM model and provided more accurate result than the ANN model. On the other hand, since the area under the receiver operating characteristic (ROC) curve in the SVM algorithm was more than this area in ANN model, it could be concluded that SVM model had higher accuracy than the ANN model.

Conclusion

According to the results, the SVM algorithm presented higher accuracy and better performance than the ANN model and was characterized with higher power and sensitivity. Overall, it provided a better classification for the prediction of CAD. The use of other data mining algorithms are suggested to improve the positive predictive value of the disease prediction.
Literature
1.
2.
Montalescot G, Sechtem U, Achenbach S, Andreotti F, Arden C, Budaj A, Bugiardini R, Crea F, Cuisset T, Di Mario C. 2013 ESC guidelines on the management of stable coronary artery disease: the task force on the management of stable coronary artery disease of the European society of cardiology. Euro Heart J. 2013;34(38):2949–3003.
3.
Kelly BB, Fuster V, editors. Promoting cardiovascular health in the developing world: a critical challenge to achieve global health. National Academies Press; 2010.
4.
Sanchis-Gomar F, Perez-Quilis C, Leischik R, Lucia A. Epidemiology of coronary heart disease and acute coronary syndrome. Ann Transl Med. 2016;4(13):256.CrossRef
5.
Mozaffarian D, Benjamin EJ, Go AS, Arnett DK, Blaha MJ, Cushman M, Das SR, De Ferranti S, Després JP, Fullerton HJ, Howard VJ. Executive summary: heart disease and stroke statistics-2016 update: a report from the American Heart Association. Circulation. 2016;133(4):447–54.CrossRef
6.
Bergh C, Fall K, Udumyan R, Sjöqvist H, Fröbert O, Montgomery S. Severe infections and subsequent delayed cardiovascular disease. Eur J Prev Cardiol. 2017;24(18):1958–66.CrossRef
7.
Longo D, Fauci A, Kasper D, Hauser S. Harrison's principles of internal medicine. 18th ed. New York: McGraw-Hill Professionals; 2011.
8.
9.
10.
Mann DL, Zipes DP, Libby P, Bonow RO. Braunwald’s heart disease e-book: a textbook of cardiovascular medicine. Elsevier health sciences. 2014;2136:861–71.
11.
U.S. National Library of medicine (NLM). Coronary artery disease (CAD) medlineplus (trusted health information for you); 2017.
12.
Kamiya K, Masuda T, Tanaka S, Hamazaki N, Matsue Y, Mezzani A, et al. Quadriceps strength as a predictor of mortality in coronary artery disease. Am J Med. 2015;128(11):1212–9.CrossRef
13.
Charchar FJ, Bloomer LD, Barnes TA, Cowley MJ, Nelson CP, Wang Y, Denniff M, Debiec R, Christofidou P, Nankervis S, Dominiczak AF. Inheritance of coronary artery disease in men: an analysis of the role of the Y chromosome. Lancet. 2012;379(9819):915–22.CrossRef
14.
Rezaei-hachesu P, Ahmadi M, Alizadeh S, Sadoughi F. Use of data mining techniques to determine and predict length of stay of cardiac patients. Healthc Inform Res. 2013;19(2):121–9.CrossRef
15.
Sudhakar K, Manimekalai DM. Study of heart disease prediction using data mining. Int Adv Res Comput Sci Soft Eng. 2014;4(1):1157-1160.
16.
Yeh DY, Cheng CH, Chen YW. A predictive model for cerebrovascular disease using data mining. Expert Syst Appl. 2011;38(7):8970–7.CrossRef
17.
Bellazzi R, Ferrazzi F, Sacchi L. Predictive data mining in clinical medicine: a focus on selected methods and applications. Wiley interdisciplinary reviews: WIREs data mining and knowledge discovery. 2011;1(5):416–30.
18.
Amato F, López A, Peña-Méndez EM, Vaňhara P, Hampl A, Havel J. Artificial neural networks in medical diagnosis. J Appl Biomed. 2013;11:47–58.CrossRef
19.
Sivagowry S, Durairaj M, Persia A. An empirical study on applying data mining techniques for the analysis and prediction of heart disease. Information communication and embedded systems (ICICES), international conference on 2013 Feb 21;265–270.
20.
Sufi F, Khalil I. Diagnosis of cardiovascular abnormalities from compressed ECG: a data mining-based approach. IEEE Trans Inf Technol Biomed. 2011;15(1):33–9.CrossRef
21.
22.
23.
Kausar N, Abdullah A, Samir BB, Palaniappan S, BS AG, Dey N. Ensemble clustering algorithm with supervised classification of clinical data for early diagnosis of coronary artery disease. J Med Imaging Health Inform. 2016;6(1):78–87.CrossRef
24.
Abawajy JH, Kelarev AV, Chowdhury M. Multistage approach for clustering and classification of ECG data. Comput Methods Prog Biomed. 2013;112(3):720–30.CrossRef
25.
Zhou X, Chen S, Liu B, Zhang R, Wang Y, Li P, Guo Y, Zhang H, Gao Z, Yan X. Development of traditional Chinese medicine clinical data warehouse for medical knowledge discovery and decision support. Artifi Intell Med. 2010;48(2–3):139–52.CrossRef
26.
Guner LA, Karabacak NI, Akdemir OU, Karagoz PS, Kocaman SA, Cengel A, Unlu M. An open-source framework of neural networks for diagnosis of coronary artery disease from myocardial perfusion SPECT. J Nucl Cardiol. 2010;17(3):405–13.CrossRef
27.
Orphanou K, Stassopoulou A, Keravnou E. DBN-extended: a dynamic Bayesian network model extended with temporal abstractions for coronary heart disease prognosis. IEEE J Biomed Health Inform. 2016;20(3):944–52.CrossRef
28.
Kim J, Lee J, Lee Y. Data-mining-based coronary heart disease risk prediction model using fuzzy logic and decision tree. Healthc Inform Res. 2015;21(3):167–74.CrossRef
29.
Karaolis MA, Moutiris JA, Hadjipanayi D, Pattichis CS. Assessment of the risk factors of coronary heart events based on data mining with decision trees. IEEE Trans Inf Technol Biomed. 2010;14(3):559–66.CrossRef
30.
Verma L, Srivastava S, Negi PC. A hybrid data mining model to predict coronary artery disease cases using non-invasive clinical data. J Med Syst. 2016;40(7):178.CrossRef
31.
Das R, Turkoglu I, Sengur A. Effective diagnosis of heart disease through neural networks ensembles. Expert Syst Appl. 2009;36(4):7675–80.CrossRef
32.
Acharya UR, Fujita H, Lih OS, Adam M, Tan JH, Chua CK. Automated detection of coronary artery disease using different durations of ECG segments with convolutional neural network. Knowl Based Syst. 2017;132:62–71.CrossRef
33.
Dolatabadi AD, Khadem SE, Asl BM. Automated diagnosis of coronary artery disease (CAD) patients using optimized SVM. Comput Methods Prog Biomed. 2017;138:117–26.CrossRef
34.
35.
Steenman M, Lande G. Cardiac aging and heart disease in humans. Biophys Rev. 2017;9(2):131–7.CrossRef
36.
Bayturan O, Kapadia S, Nicholls SJ, Tuzcu EM, Shao M, Uno K, Shreevatsa A, Lavoie AJ, Wolski K, Schoenhagen P. Clinical predictors of plaque progression despite very low levels of low-density lipoprotein cholesterol. J of the American Col of Cardio. 2010;55(24):2736–42.CrossRef
37.
Nicholls SJ, Hsu A, Wolski K, Hu B, Bayturan O, Lavoie A, et al. Intravascular ultrasound-derived measures of coronary atherosclerotic plaque burden and clinical outcome. J Am Coll Cardiol. 2010;55(21):2399–407.CrossRef
38.
Murthy VL, Naya M, Foster CR, Gaber M, Hainer J, Klein J, et al. Association between coronary vascular dysfunction and cardiac mortality in patients with and without diabetes mellitus. Circulation. 2012;126(15):1858–68 CIRCULATIONAHA. 112.CrossRef
39.
Perk J, De Backer G, Gohlke H, Graham I, Reiner Z, Verschuren M, et al. European Association for Cardiovascular Prevention & rehabilitation (EACPR); ESC Committee for practice guidelines (CPG). European guidelines on cardiovascular disease prevention in clinical practice (ver 2012). The fifth joint task force of the European Society of Cardiology and Other Societies on cardiovascular disease prevention in clinical practice (constituted by representatives of nine societies and by invited experts). Euro Heart J. 2012;33(13):1635–701.CrossRef
40.
Frey P, Waters DD, DeMicco DA, Breazna A, Samuels L, Pipe A, et al. Impact of smoking on cardiovascular events in patients with coronary disease receiving contemporary medical therapy (from the treating to new targets (TNT) and the incremental decrease in end points through aggressive lipid lowering (IDEAL) trials). Am J Cardiol. 2011;107(2):145–50.CrossRef
41.
Yiu KH, de Graaf FR, Schuijf JD, van Werkhoven JM, Marsan NA, Veltman CE, de Roos A, Pazhenkottil A, Kroft LJ, Boersma E, Herzog B. Age-and gender-specific differences in the prognostic value of CT coronary angiography. Heart. 2012;98(3):232–7.CrossRef
42.
Masethe HD, Masethe MA. Prediction of heart disease using classification algorithms. In: Proceedings of the world congress on engineering and computer science. San Fransico: WCECS; 2014. p. 22–4.
43.
Kivimäki M, Nyberg ST, Batty GD, Fransson EI, Heikkilä K, Alfredsson L, et al. Job strain as a risk factor for coronary heart disease: a collaborative meta-analysis of individual participant data. Lancet. 2012;380(9852):1491–7.CrossRef
44.
Kermani M, Jonidi Jaffar A, Dowlati M, Rezaei Kalantari R. Number of total mortality, cardiovascular mortality and chronic obstructive pulmonary disease due to exposure with nitrogen dioxide in Tehran during 2005-2014. Urmia Med J. 2017;28(4):22.CrossRef
45.
Zhang Q, Cheng L, Boutaba R. Cloud computing: state-of-the-art and research challenges. J Internet Ser Appl. 2010;1(1):7–18.CrossRef
46.
Vanos JK, Hebbern C, Cakmak S. Risk assessment for cardiovascular and respiratory mortality due to air pollution and synoptic meteorology in 10 Canadian cities. Environ Pollut. 2014;185:322–32.CrossRef
47.
Garcia MC, Faul M, Massetti G, Thomas CC, Hong Y, Bauer UE, Iademarco MF. Reducing potentially excess deaths from the five leading causes of death in the rural United States. MMWR Surveill Summ. 2017;66(2):1.CrossRef
48.
Hoseini K, Sadeghian S, Mahmoudian M, Hamidian R, Abbasi A. Family history of cardiovascular disease as a risk factor for coronary artery disease in adult offspring. Monaldi Arch Chest Dis. 2016;70(2):84-87.
49.
Siervo M, Lara J, Chowdhury S, Ashor A, Oggioni C, Mathers JC. Effects of the dietary approach to stop hypertension (DASH) diet on cardiovascular risk factors: a systematic review and meta-analysis. Br J Nutr. 2015;113(1):1–5.CrossRef
50.
Mahmoodi K, Nasehi L, Karami E, Soltanpour MS. Association of nitric oxide levels and endothelial nitric oxide synthase G894T polymorphism with coronary artery disease in the Iranian population. Vasc Specialist Int. 2016;32(3):105.CrossRef
51.
Andria N, Nassar A, Kusniec F, Ghanim D, Qarawani D, Kachel E, et al. Ethnicity of symptomatic coronary artery disease referred for coronary angiography in the galilee: prevalence, risk factors, and a case for screening and modification. Isr Med Assoc J. 2018;20(3):182–5.PubMed
52.
Meyers DG, Neuberger JS, He J. Cardiovascular effect of bans on smoking in public places: a systematic review and meta-analysis. J Am Coll Cardiol. 2009;54(14):1249–55.CrossRef
53.
Lv S, Liu W, Zhou Y, Liu Y, Shi D, Zhao Y, Liu X. Hyperuricemia and smoking in young adults suspected of coronary artery disease [less than or equal to] 35 years of age: a hospital-based observational study. BMC Cardiovasc Disord. 2018;18(1):178.CrossRef
54.
Campo G, Pavasini R, Malagù M, Mascetti S, Biscaglia S, Ceconi C, et al. Chronic obstructive pulmonary disease and ischemic heart disease comorbidity: overview of mechanisms and clinical management. Cardiovasc Drugs Ther. 2015;29(2):147–57.CrossRef
55.
Jahangir E, De Schutter A, Lavie CJ. The relationship between obesity and coronary artery disease. Transl Res. 2014;164(4):336–44.CrossRef
56.
Rairikar A, Kulkarni V, Sabale V, Kale H, Lamgunde A. Heart disease prediction using data mining techniques. In Intelligent computing and control (I2C2), IEEE international conference on 2017 Jun: 1–8.
57.
Uğuz H. A biomedical system based on artificial neural network and principal component analysis for diagnosis of the heart valve diseases. J Med Syst. 2012;36(1):61–72.CrossRef
58.
Wertli MM, Ruchti KB, Steurer J, Held U. Diagnostic indicators of non-cardiovascular chest pain: a systematic review and meta-analysis. BMC Med. 2013;11(1):239.CrossRef
59.
Kurt I, Ture M, Kurum AT. Comparing performances of logistic regression, classification and regression tree, and neural networks for predicting coronary artery disease. Expert Syst Appl. 2008;34(1):366–74.CrossRef
60.
61.
Maglogiannis I, Loukis E, Zafiropoulos E, Stasis A. Support vectors machine-based identification of heart valve diseases using heart sounds. Comput Methods Prog Biomed. 2009;95(1):47–61.CrossRef
62.
Ghumbre S, Patil C, Ghatol A. Heart disease diagnosis using support vector machine. International conference on computer science and information technology (ICCSIT’). Pattaya; 2011.
63.
Hanbay D. An expert system based on least square support vector machines for diagnosis of the valvular heart disease. Expert Syst Appl. 2009;36(3):4232–8.CrossRef
64.
Babaoglu I, Findik O, Ülker E. A comparison of feature selection models utilizing binary particle swarm optimization and genetic algorithm in determining coronary artery disease using support vector machine. Expert Syst Appl. 2010;37(4):3177–83.CrossRef
Metadata
Title
Predicting coronary artery disease: a comparison between two data mining algorithms
Authors
Haleh Ayatollahi
Leila Gholamhosseini
Masoud Salehi
Publication date
01-12-2019
Publisher
BioMed Central
Published in
BMC Public Health / Issue 1/2019
Electronic ISSN: 1471-2458
DOI
https://doi.org/10.1186/s12889-019-6721-5

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