Abstract
Ensemble methods overcome the limitations of single machine learning techniques by combining different techniques, and are employed in the quest to achieve a high level of accuracy. This approach has been investigated in various fields, one of them being that of bioinformatics. One of the most frequent applications of ensemble techniques involves research into cardiovascular diseases, which are considered the leading cause of death worldwide. The purpose of this research work is to identify the papers that investigate ensemble classification techniques applied to cardiology diseases, and to analyse them according to nine aspects: their publication venues, the medical tasks tackled, the empirical and research types adopted, the types of ensembles proposed, the single techniques used to construct the ensembles, the validation frameworks adopted to evaluate the proposed ensembles, the tools used to build the ensembles, and the optimization methods employed for the single techniques. This paper reports the carrying out of a systematic mapping study. An extensive automatic search in four digital libraries: IEEE Xplore, ACM Digital Library, PubMed, and Scopus, followed by a study selection process, resulted in the identification of 351 papers that were used to address our mapping questions. This study found that the papers selected had been published in a large number of different resources. The medical task addressed most frequently by the selected studies was diagnosis. In addition, the experiment-based empirical type and evaluation-based research type were the most dominant approaches adopted by the selected studies. Homogeneous ensembles were the ensemble type that was developed most often in literature, while decision trees, artificial neural networks and Bayesian classifiers were the single techniques used most frequently to develop ensemble classification methods. The weighted majority and majority voting rules were adopted to obtain the final decision of the ensembles developed. With regard to evaluation frameworks, the datasets obtained from the UCI and PhysioBank repositories were those used most often to evaluate the ensemble methods, while the k-fold cross-validation method was the most frequently-employed validation technique. Several tools with which to build ensemble classifiers were identified, and the type of software adopted with the greatest frequency was open source. Finally, only a few researchers took into account the optimization of the parameter settings of either single or meta ensemble classifiers. This mapping study attempts to provide a greater insight into the application of ensemble classification methods in cardiovascular diseases. The majority of the selected papers reported positive feedback as regards the ability of ensemble methods to perform better than single methods. Further analysis is required to aggregate the evidence reported in literature.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abawajy J, Kelarev A, Chowdhury MU, Herbert FJ (2016) Enhancing predictive accuracy of cardiac autonomic neuropathy using blood biochemistry features and iterative multitier ensembles. IEEE J Biomed Heal Inform 20(1):408–415. https://doi.org/10.1109/JBHI.2014.2363177
Ahmad T et al (2018) Machine learning methods improve prognostication, identify clinically distinct phenotypes, and detect heterogeneity in response to therapy in a large cohort of heart failure patients. J Am Heart Assoc 7(8):1–15. https://doi.org/10.1161/JAHA.117.008081
Ahmed H, Younis EMG, Hendawi A, Ali AA (2019) Heart disease identification from patients’ social posts, machine learning solution on Spark. Futur Gener Comput Syst. https://doi.org/10.1016/j.future.2019.09.056
Aksela M, Laaksonen J (2006) Using diversity of errors for selecting members of a committee classifier. Pattern Recognit 39(4):608–623. https://doi.org/10.1016/j.patcog.2005.08.017
Alizadehsani R et al (2013) A data mining approach for diagnosis of coronary artery disease. Comput Methods Programs Biomed 111(1):52–61. https://doi.org/10.1016/j.cmpb.2013.03.004
Allyn J et al (2017) A comparison of a machine learning model with EuroSCORE II in predicting mortality after elective cardiac surgery: a decision curve analysis. PLoS ONE 12(1):e0169772. https://doi.org/10.1371/journal.pone.0169772
Amjad M, Rafiq A, Ali Z, Akhtar N, Abbas A, Israr-Ur-Rehman A (2019) Empirical performance analysis of decision tree and support vector machine based classifiers on biological databases. Int J Adv Comput Sci Appl 10(9):309–318. https://doi.org/10.14569/ijacsa.2019.0100940
Ani R, Augustine A, Akhil NC, Deepa OS (2016) Random forest ensemble classifier to predict the coronary heart disease using risk factors. In: Suresh LP, Panigrahi BK (eds) Advances in intelligent systems and computing, vol 397. Springer, New Delhi, pp 701–710
Aruna S, Nandakishore LV (2015) Ensemble neural network algorithm for detecting cardiac arrhythmia. In: Suresh LP, Dash SS, Panigrahi BK (eds) Advances in intelligent systems and computing, vol 324. Springer, New Delhi, pp 27–35
Bashir S, Qamar U, Khan FH (2015) BagMOOV: a novel ensemble for heart disease prediction bootstrap aggregation with multi-objective optimized voting. Aust Phys Eng Sci Med 38(2):305–323. https://doi.org/10.1007/s13246-015-0337-6
Bashir S, Qamar U, Khan FH (2016a) A multicriteria weighted vote-based classifier ensemble for heart disease prediction. Comput Intell 32(4):615–645. https://doi.org/10.1111/coin.12070
Bashir S, Qamar U, Khan FH (2016b) IntelliHealth: a medical decision support application using a novel weighted multi-layer classifier ensemble framework. J Biomed Inform 59:185–200. https://doi.org/10.1016/j.jbi.2015.12.001
Bennett J, Matthews A (2013) World medical association declaration of Helsinki. JAMA 310(20):2191. https://doi.org/10.1001/jama.2013.281053
Bilgin F, Kuntalp M (2017) Paroxysmal atrial fibrillation (PAF) screening by ensemble learning. In: Proceedings of the 2017 5th international symposium on electrical and electronics engineering, ISEEE 2017, vol 2017-Dec, pp 1–5. https://doi.org/10.1109/ISEEE.2017.8170658
Booba B, Gopal TV (2013) Comparison of Ant Colony Optimization & Particle Swarm Optimization in Grid Environment. Int J Adv Res Comput Sci Appl 1(5):27–33
Boublenza A, Chikh MA, Bouchikhi S (2015) Classifier set selection for cardiac arrhythmia recognition using diversity. J Med Imaging Heal Inform 5(3):513–519. https://doi.org/10.1166/jmihi.2015.1413
Breiman L (1996) Bagging predictors. Mach Learn 26(2):123–140. https://doi.org/10.1023/A:1018054314350
Budnik M, Krawczyk B (2013) On optimal settings of classification tree ensembles for medical decision support. Health Inf J 19(1):3–15. https://doi.org/10.1177/1460458212446096
Chen X, Ji J, Loparo K, Li P (2017) Real-time personalized cardiac arrhythmia detection and diagnosis: a cloud computing architecture. In: 2017 IEEE EMBS international conference on biomedical & health informatics (BHI), pp 201–204. 10.1109/BHI.2017.7897240
Choudhury AD, Banerjee R, Pal A, Mandana KM (2017) A fusion approach for non-invasive detection of coronary artery disease. In: Proceedings of the 11th EAI international conference on pervasive computing technologies for healthcare—PervasiveHealth ’17, pp 217–220. https://doi.org/10.1145/3154862.3154871
Dai W, Brisimi TS, Adams WG, Mela T, Saligrama V, Paschalidis IC (2015) Prediction of hospitalization due to heart diseases by supervised learning methods. Int J Med Inf 84(3):189–197. https://doi.org/10.1016/j.ijmedinf.2014.10.002
Das R, Turkoglu I, Sengur A (2009a) Effective diagnosis of heart disease through neural networks ensembles. Expert Syst Appl 36(4):7675–7680. https://doi.org/10.1016/j.eswa.2008.09.013
Das R, Turkoglu I, Sengur A (2009) Diagnosis of valvular heart disease through neural networks ensembles. Comput Methods Programs Biomed 93(2):185–191. https://doi.org/10.1016/j.cmpb.2008.09.005
Davis J, Goadrich M (2006) The relationship between precision-recall and ROC curves. In: Proceedings of the 23rd international conference on Machine learning
Du G, Su F, Cai A (2009) Face recognition using SURF features. Proc SPIE Int Soc Opt Eng 8(1):749628. https://doi.org/10.1117/12.832636
Durlak F, Wels M, Schwemmer C, Sühling M, Steidl S, Maier A (2017) Growing a random forest with fuzzy spatial features for fully automatic artery-specific coronary calcium scoring. Lect Notes Comput Sci 15(3):27–35
El Bialy R, Salama MA, Karam O (2016) An ensemble model for Heart disease data sets: a generalized model. In: Proceedings of the 10th international conference on informatics and systems—INFOS’16, 2016, vol 9–11-May, pp 191–196. https://doi.org/https://doi.org/10.1145/2908446.2908482.
Elish MO, Helmy T, Hussain MI (2013) Empirical study of homogeneous and heterogeneous ensemble models for software development effort estimation. Math Probl Eng. https://doi.org/10.1155/2013/312067
Eom J, Kim S, Zhang B (2008) AptaCDSS-E: a classifier ensemble-based clinical decision support system for cardiovascular disease level prediction. Expert Syst Appl 34(4):2465–2479. https://doi.org/10.1016/j.eswa.2007.04.015
Esfandiari N, Babavalian MR, Moghadam AME, Tabar VK (2014) Knowledge discovery in medicine: current issue and future trend. Expert Syst Appl 41(9):4434–4463. https://doi.org/10.1016/j.eswa.2014.01.011
Faust O et al (2017) Computer aided diagnosis of Coronary Artery Disease, Myocardial Infarction and carotid atherosclerosis using ultrasound images: a review. Phys Med 33:1–15. https://doi.org/10.1016/j.ejmp.2016.12.005
Faust O, Ng EYK (2016) Computer aided diagnosis for cardiovascular diseases based on ECG signals: a survey. J Mech Med Biol 16(01):1640001. https://doi.org/10.1142/S0219519416400017
Ferri C, Hernández-Orallo J, Modroiu R (2009) An experimental comparison of performance measures for classification. Pattern Recognit Lett 30(1):27–38. https://doi.org/10.1016/j.patrec.2008.08.010
Gatsios D et al (2010) Knowledge extraction in a population suffering from heart failure. In: Proceedings of the 10th IEEE international conference on information technology and applications in biomedicine, pp 1–6. https://doi.org/10.1109/ITAB.2010.5687684
Gayathri P, Jaisankar N (2013) Comprehensive study of heart disease diagnosis using data mining and soft computing techniques. Int J Eng Technol 5(3):2947–2958
Gomes EF, Jorge AM, Azevedo PJ (2014) Classifying heart sounds using SAX motifs, random forests and text mining techniques. In: Proceedings of the 18th international database engineering & applications symposium on—IDEAS ’14, pp 334–337. https://doi.org/10.1145/2628194.2628240
Guidi G, Pettenati MC, Melillo P, Iadanza E (2014) A machine learning system to improve heart failure patient assistance. IEEE J Biomed Heal Inf 18(6):1750–1756. https://doi.org/10.1109/JBHI.2014.2337752
Guidi G, Pollonini L, Dacso CC, Iadanza E (2015) A multi-layer monitoring system for clinical management of Congestive Heart Failure. BMC Med Inform Decis Mak 15(S3):S5. https://doi.org/10.1186/1472-6947-15-S3-S5
Gupta D, Khare S, Aggarwal A (2017) A method to predict diagnostic codes for chronic diseases using machine learning techniques. In: Proceeding of the IEEE international conference on computing, communication and automation, ICCCA 2016, pp 281–287. https://doi.org/10.1109/CCAA.2016.7813730
Hansen LK, Salamon P (1990) Neural network ensembles. IEEE Trans Pattern Anal Mach Intell 12(10):993–1001. https://doi.org/10.1109/34.58871
Haque MN, Noman MN, Berretta R, Moscato P (2016) Optimising weights for heterogeneous ensemble of classifiers with differential evolution. In: 2016 IEEE congress on evolutionary computation, CEC 2016, pp 233–240. https://doi.org/10.1109/CEC.2016.7743800
Hasan SMM, Mamun MA, Uddin MP, Hossain MA (2018) Comparative analysis of classification approaches for heart disease prediction. In: 2018 international conference on computing communication chemistry electronic & engineering materials, pp 1–4
Hassan R et al (2005) A comparison of particle swarm optimization and the genetic algorithm. In: AIAA paper 2005-1897, no April, pp 1–13, https://doi.org/10.2514/6.2005-1897
Hijazi S, Page A, Kantarci B, Soyata T (2016) Machine learning in cardiac health monitoring and decision support. Computer (Long Beach, CA) 49(11):38–48. https://doi.org/10.1109/MC.2016.339
Ho TK (1998) The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell 20(8):832–844. https://doi.org/10.1109/34.709601
Hosni M, Idri A (2018) Software development effort estimation using feature selection techniques. Front Artif Intell Appl 303(November):439–452. https://doi.org/10.3233/978-1-61499-900-3-439
Hosni M, Idri A, Abran A, Nassif AB (2018) On the value of parameter tuning in heterogeneous ensembles effort estimation. Soft Comput 22(18):5977–6010. https://doi.org/10.1007/s00500-017-2945-4
Hosni M, Idri A (2017) Software effort estimation using classical analogy ensembles based on random subspace. In: Proceedings of the ACM symposium on applied computing, 2017, vol Part F1280. https://doi.org/10.1145/3019612.3019784.
Hosni M, Idri A, Nassif AB, Abran A (2017) Heterogeneous ensembles for software development effort estimation. In: Proceedings of the 2016 3rd international conference on soft computing and machine intelligence, ISCMI 2016. https://doi.org/10.1109/ISCMI.2016.15
Hosni M, Idri A, Abran A (2017) Investigating heterogeneous ensembles with filter feature selection for software effort estimation. In: Proceedings of the 27th international workshop on software measurement and 12th international conference on software process and product measurement, pp 207–220. https://doi.org/10.1145/3143434.3143456
Huang H, Hu G, Zhu L (2010) Ensemble of support vector machines for heartbeat classification. In: IEEE 10th international conference on signal processing proceedings, pp 1327–1330. https://doi.org/10.1109/ICOSP.2010.5657034
Idri A, Hosni M, Abnane I, Carrillo de Gea JM, Fernández Alemán JL (2019) Impact of parameter tuning on machine learning based breast cancer classification. Adv Intell Syst Comput 932:115–125. https://doi.org/10.1007/978-3-030-16187-3_12
Idri A, Hosni M, Abran A (2016) Systematic literature review of ensemble effort estimation. J Syst Softw 118:151–175. https://doi.org/10.1016/j.jss.2016.05.016
Idri A, Hosni M, Abran A (2016) Improved estimation of software development effort using classical and fuzzy analogy ensembles. Appl Soft Comput. https://doi.org/10.1016/j.asoc.2016.08.012
Idri A, Hosni M, Abran A (2016) Systematic mapping study of ensemble effort estimation. In: Proceedings of the 11th international conference on evaluation of novel software approaches to software engineering, 2016, no Enase, pp 132–139. https://doi.org/10.5220/0005822701320139.
Jabbar MA, Deekshatulu BL, Chandra P (2016) Prediction of heart disease using random forest and feature subset selection. Adv Intell Syst Comput 424:187–196
Jadhav S, Nalbalwar S, Ghatol A (2014) Feature elimination based random subspace ensembles learning for ECG arrhythmia diagnosis. Soft Comput 18(3):579–587. https://doi.org/10.1007/s00500-013-1079-6
Jadhav SM, Nalbalwar SL, Ghatol AA (2010) ECG arrhythmia classification using modular neural network model. In Proceedings of the 2010 IEEE EMBS conference on biomedical engineering science IECBES 2010, no December, pp 62–66. https://doi.org/10.1109/IECBES.2010.5742200.
Javadi M (2013) Combining neural networks and ANFIS classifiers for supervised examining of electrocardiogram beats. J Med Eng Technol 37(8):484–497. https://doi.org/10.3109/03091902.2013.831493
Jovic A, De Luca N, Pecchia L, Melillo P (2015) Automatic classifier based on heart rate variability to identify fallers among hypertensive subjects. Healthc Technol Lett 2(4):89–94. https://doi.org/10.1049/htl.2015.0012
Kadi I, Idri A, Fernandez-Aleman JL (2017) Systematic mapping study of data mining-based empirical studies in cardiology. Health Inform J 10:146045821771763. https://doi.org/10.1177/1460458217717636
Kadi I, Idria A (2016) Knowledge discovery in cardiology: a systematic literature review. Int J Med Inform. https://doi.org/10.1016/j.ijmedinf.2016.09.005
Karanasiou GS et al (2016) Predicting adherence of patients with HF through machine learning techniques. Healthc Technol Lett 3(3):165–170. https://doi.org/10.1049/htl.2016.0041
Kitchenham B, Charters S (2007) Guidelines for performing systematic literature reviews in software engineering. Engineering 2:1051. https://doi.org/10.1145/1134285.1134500
Kuan MM, Lim CP, Morad N, Harrison RF (2000) An experimental study of original and ordered fuzzy ARTMAP neural networks in pattern classification tasks. In 2000 TENCON proceedings of the intelligent systems and technologies for the new millennium (Cat. No. 00CH37119), vol 2, pp 392–397. https://doi.org/10.1109/TENCON.2000.888769
Kumar A, Sarkar BK (2018) A hybrid predictive model integrating C4.5 and decision table classifiers for medical data sets. J Inf Technol Res 11(2):150–167. https://doi.org/10.4018/JITR.2018040109
Kumar SS, Shaikh T (2017) Empirical evaluation of the performance of feature selection approaches on random forest. In: 2017 international conference on computer and applications (ICCA), pp 227–231. https://doi.org/10.1109/COMAPP.2017.8079769
Lab tests online (2019) https://labtestsonline.org/conditions/cardiovascular-disease
Lafta R, Zhang J, Tao X, Li Y, Abbas W (2017) A fast Fourier transform-coupled machine learning-based ensemble model for disease risk prediction using a real-life dataset. In: Lecture notes in computer science, vol 2, pp 654–670
Lavanya D (2012) Ensemble decision tree classifier for breast cancer data. Int J Inf Technol Converg Serv 2(1):17–24. https://doi.org/10.5121/ijitcs.2012.2103
Liu N, Cao J, Koh ZX, Lin Z, Ong MEH (2015) Analysis of patient outcome using ECG and extreme learning machine ensemble. In: 2015 IEEE international conference on digital signal processing (DSP), vol 2015-Sept, pp 1049–1052. https://doi.org/10.1109/ICDSP.2015.7252038
Lofaro D, Groccia MC, Guido R, Conforti D, Caroleo S, Fragomeni G (2016) Machine learning approaches for supporting patient: specific cardiac rehabilitation programs. In: 2016 computing in cardiology conference (CinC), pp 3–6. https://doi.org/10.23919/CIC.2016.7868701
Loh BCS, Then PHH (2017) Deep learning for cardiac computer-aided diagnosis: benefits, issues & solutions. mHealth 3:45–45. https://doi.org/10.21037/mhealth.2017.09.01
Luo Y et al (2017) Predicting congenital heart defects: a comparison of three data mining methods. PLoS ONE 12(5):e0177811. https://doi.org/10.1371/journal.pone.0177811
Meesri S, Phimoltares S (2017) Diagnosis of heart disease using a mixed classifier. In: 2017 21st international computing science engineering conference, vol 6, pp 1–5
Mualla Y et al (2019) Agent-based simulation of unmanned aerial vehicles in civilian applications: a systematic literature review and research directions. Futur Gener Comput Syst 100:344–364. https://doi.org/10.1016/j.future.2019.04.051
Mustaqeem A, Anwar SM, Khan AR, Majid M (2017) A statistical analysis based recommender model for heart disease patients. Int J Med Inf 108(July):134–145. https://doi.org/10.1016/j.ijmedinf.2017.10.008
Nguyen TT, Liew AWC, Tran MT, Pham XC, Nguyen MP (2014) A novel genetic algorithm approach for simultaneous feature and classifier selection in multi classifier system. In: Proceedings of the 2014 IEEE congress on evolutionary computation, CEC 2014, pp 1698–1705. https://doi.org/10.1109/CEC.2014.6900377
Nikookar E, Naderi E (2018) Hybrid ensemble framework for heart disease detection and prediction. Int J Adv Comput Sci Appl 9(5):243–248. https://doi.org/10.14569/IJACSA.2018.090533
Nita S, Bitam S, Mellouk A (2018) An enhanced random forest for cardiac diseases identification based on ECG signal. In: 2018 14th international wireless communications & mobile computing conference, pp 1339–1344
Pandit D, Zhang L, Aslam N, Liu C, Hossain A, Chattopadhyay S (2014) An efficient abnormal beat detection scheme from ECG signals using neural network and ensemble classifiers. In: The 8th international conference on software, knowledge, information management and applications (SKIMA 2014), pp 1–6. https://doi.org/10.1109/SKIMA.2014.7083561
Pawlovsky AP (2018) An ensemble based on distances for a kNN method for heart disease diagnosis. In: 2018 international conference on electronics, information, and communication (ICEIC), vol 2018-Jan, pp 1–4. https://doi.org/10.23919/ELINFOCOM.2018.8330570
Petersen K, Vakkalanka S, Kuzniarz L (2015) Guidelines for conducting systematic mapping studies in software engineering: an update. Inf Softw Technol 64:1–18. https://doi.org/10.1016/j.infsof.2015.03.007
Petersen K, Feldt R, Mujtaba S, Mattsson M (2008) Systematic mapping studies in software engineering. In: 12th international conference on evaluation and assessment in software engineering, vol 17, p 10. https://doi.org/10.1142/S0218194007003112
Potes C, Parvaneh S, Rahman A, Conroy B (2016) Ensemble of feature-based and deep learning: based classifiers for detection of abnormal heart sounds. In: 2016 computing in cardiology conference (CinC), pp 621–624. https://doi.org/10.22489/CinC.2016.182-399
Pławiak P (2018) Novel genetic ensembles of classifiers applied to myocardium dysfunction recognition based on ECG signals. Swarm Evol Comput 39(July):192–208. https://doi.org/10.1016/j.swevo.2017.10.002
Rosiek A, Leksowski K (2016) The risk factors and prevention of cardiovascular disease: the importance of electrocardiogram in the diagnosis and treatment of acute coronary syndrome. Ther Clin Risk Manag 12:1223–1229. https://doi.org/10.2147/TCRM.S107849
Ruta D, Gabrys B (2000) An overview of classifier fusion methods. Comput Inf Syst 7:1–10
Sahin H, Subasi A (2015) Classification of the cardiotocogram data for anticipation of fetal risks using machine learning techniques. Appl Soft Comput 33:231–238. https://doi.org/10.1016/j.asoc.2015.04.038
Sakellarios A et al (2019) A novel concept of the management of coronary artery disease patients based on machine learning risk stratification and computational biomechanics: preliminary results of SMARTool project antonis. In: World congress on medical physics & biomedical engineering (IUPESM), Prague, Czech Republic, 2019, vol 68/1, no May, pp 731–735. https://doi.org/10.1007/978-981-10-9035-6
Sasikala S, Appavu Alias Balamurugan S, Geetha S (2013) An efficient feature selection paradigm using PCA-CFS-Shapley values ensemble applied to small medical data sets. In: 2013 fourth international conference on computing, communications and networking technologies (ICCCNT), pp 1–5. https://doi.org/10.1109/ICCCNT.2013.6726773
Schapire RE (1990) The strength of weak ties. J Mach Learn 1:197–227. https://doi.org/10.1023/A:1022648800760
Schapire RE (2003) Measures of diversity in classifier ensembles. Mach Learn 51(2):181–207. https://doi.org/10.1049/ic:20010105
Schapire RE (1999) A brief introduction to boosting. In: Proceedings of the sixth international joint conference artificial intelligence
Schlemmer A, Zwirnmann H, Zabel M, Parlitz U, Luther S (2014) Evaluation of machine learning methods for the long-term prediction of cardiac diseases. In: 2014 8th conference of the European study group on cardiovascular oscillations (ESGCO), no Esgco, pp 157–158. https://doi.org/10.1109/ESGCO.2014.6847567
Seni G, Elder JF (2010) Ensemble methods in data mining: improving accuracy through combining predictions, vol 2. Morgan & Claypool Publishers, New York
Shah SAA, Aziz W, Arif M, Nadeem MSA (2015) Decision trees based classification of cardiotocograms using bagging approach. In: 2015 13th international conference on frontiers of information technology (FIT), pp 12–17. https://doi.org/10.1109/FIT.2015.14.
Sharma R, Singh SN, Khatri S (2019) Data mining classification techniques—comparison for better accuracy in prediction of cardiovascular disease. Int J Data Anal Tech Strateg 11(4):356–373. https://doi.org/10.1504/IJDATS.2019.103756
Smolen D (2017) Atrial fibrillation detection using boosting and stacking ensemble. In: 2017 computing in cardiology conference (CinC), vol 44, pp 2–5, https://doi.org/10.22489/CinC.2017.068-247
Soria-Olivas E, Martin-Guerrero JD, Redon J, Tellez-Plaza M, Vila-Frances J (2015) Improving mortality prediction in cardiovascular risk patients by balancing classes. In: 2015 IEEE international conference on data mining workshop (ICDMW), pp 480–484. 10.1109/ICDMW.2015.76
Srimani PK, Koti MS, Patel RB, Singh BP (2011) A comparison of different learning models used in data mining for medical data. AIP Conf Proc 1414:51–55. https://doi.org/10.1063/1.3669930
Tan C, Chen H, Xia C (2009) The prediction of cardiovascular disease based on trace element contents in hair and a classifier of boosting decision stumps. Biol Trace Elem Res 129(1–3):9–19. https://doi.org/10.1007/s12011-008-8279-4
Tike Thein HT, Mo Tun KM (2015) An approach for breast cancer diagnosis classification using neural network. Adv Comput Int J 6(1):1–11. https://doi.org/10.5121/acij.2015.6101
Tsipouras MG et al (2013) Adverse event prediction in patients with left ventricular assist devices. In: 2013 35th annual international conference of the IEEE engineering in medicine and biology society (EMBC), no v, pp 1314–1317. https://doi.org/10.1109/EMBC.2013.6609750
Tu MC, Shin D, Shin DK (2009) Effective diagnosis of heart disease through bagging approach. In: Proceedings of the 2009 2nd international conference on biomedical engineering and informatics, BMEI 2009, vol 11, no 9, pp 1–4. https://doi.org/10.1109/BMEI.2009.5301650.
Tulu B, Djamasbi S, Leroy G (2019) Designing a machine learning model to predict cardiovascular disease without any blood test. In: Extending the boundaries of design science theory and practice, vol 11491. Springer, p 324
Valdovinos RM, Sanchez JS (2005) Class-dependant resampling for medical applications. In: Fourth international conference on machine learning and applications (ICMLA’05), vol 2005, pp 351–356. https://doi.org/10.1109/ICMLA.2005.15
Vapnik VN (1998) Statistical learning theory. Wiley, New York
Vapnik VN (1999) An overview of statistical learning theory. IEEE Trans Neural Netw 10(5):988–999. https://doi.org/10.1109/72.788640
Wang H, Zheng B, Yoon SW, Ko HS (2018) A support vector machine-based ensemble algorithm for breast cancer diagnosis. Eur J Oper Res 267(2):687–699. https://doi.org/10.1016/j.ejor.2017.12.001
Wang S, Yao X (2009) Diversity analysis on imbalanced data sets by using ensemble models. In: 2009 IEEE symposium on computational intelligence and data mining, pp 324–331. https://doi.org/10.1109/CIDM.2009.4938667
Wen J, Li S, Lin Z, Hu Y, Huang C (2012) Systematic literature review of machine learning based software development effort estimation models. Inf Softw Technol 54(1):41–59. https://doi.org/10.1016/j.infsof.2011.09.002
Weng SF, Reps J, Kai J, Garibaldi JM, Qureshi N (2017) Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLoS ONE 12(4):e0174944. https://doi.org/10.1371/journal.pone.0174944
Wieringa R, Maiden ÆN, Mead ÆN (2006) Requirements engineering paper classification and evaluation criteria : a proposal and a discussion. Requir Eng. https://doi.org/10.1007/s00766-005-0021-6
Wong TT (2015) Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation. Pattern Recognit 48(9):2839–2846. https://doi.org/10.1016/j.patcog.2015.03.009
World Health Organization (WHO) (2017) https://www.who.int/en/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)
Xiao Y, Fang R (2017) RFMiner: risk factors discovery and mining for preventive cardiovascular health. In: 2017 IEEE/ACM international conference on connected health: applications, systems and engineering technologies (CHASE), pp 278–279. https://doi.org/10.1109/CHASE.2017.101
Yang X, Yuan B, Liu W (2009) Dynamic weighting ensembles for incremental learning. In: Proceedings of the 2009 Chinese conference on pattern recognition, CCPR 2009, and the 1st CJK joint workshop on pattern recognition, CJKPR, pp 98–102. https://doi.org/10.1109/CCPR.2009.5344129
Yun J, Zhanhuai L, Yong W, Longbo Z (2006) A better classifier based on rough set and neural network for medical images. In: Sixth IEEE international conference data mining—work, pp 853–857. https://doi.org/10.1109/ICDMW.2006.1
Yıldız OT, İrsoy O, Alpaydın E (2016) Bagging soft decision trees. In: Machine learning for health informatics, vol 1, pp 25–36
Zeng XD, Chao S, Wong F (2011) Ensemble learning on heartbeat type classification. In: Proceedings 2011 international conference on system science and engineering, no June, pp 320–325. https://doi.org/10.1109/ICSSE.2011.5961921
Zhang J et al (2017) Coupling a fast Fourier transformation with a machine learning ensemble model to support recommendations for heart disease patients in a telehealth environment. IEEE Access 5(c):10674–10685. https://doi.org/10.1109/ACCESS.2017.2706318
Zhang Z, Luo X (2014) Heartbeat classification using decision level fusion. Biomed Eng Lett 4(4):388–395. https://doi.org/10.1007/s13534-014-0158-7
Zhang Y, Zhao Z (2018) Fetal state assessment based on cardiotocography parameters using PCA and AdaBoost. In: Proceedings of the 2017 10th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2017, vol 2018-Jan, pp 1–6. https://doi.org/10.1109/CISP-BMEI.2017.8302314
Zhao H et al (2011) Discovery of diagnosis pattern of coronary heart disease with Qi deficiency syndrome by the T -test-based adaboost algorithm. Evid Based Compl Altern Med 2011:1–7. https://doi.org/10.1155/2011/408650
Zhao Z, Zhang Y, Deng Y (2018) A comprehensive feature analysis of the fetal heart rate signal for the intelligent assessment of fetal state. J Clin Med 7(8):223. https://doi.org/10.3390/jcm7080223
Zhou Z-H (2012) Ensemble methods. CRC Press, Boca Raton
Zolfaghar K, Meadem N, Teredesai A, Roy SB, Chin S-C, Muckian B (2013) Big data solutions for predicting risk-of-readmission for congestive heart failure patients. In: 2013 IEEE international conference on big data, vol 3, no 2, pp 64–71. https://doi.org/10.1109/BigData.2013.6691760
Acknowledgements
The work of the first author is partially supported by European Commission through the Erasmus+ International Mobility Program (KA107). This work was partly supported by the Spanish MICINN, as well as European Commission FEDER funds, under Grants RTI2018-098156-B-C53 and RTI2018-098309-B-C33.
Author information
Authors and Affiliations
Corresponding author
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Hosni, M., Carrillo de Gea, J.M., Idri, A. et al. A systematic mapping study for ensemble classification methods in cardiovascular disease. Artif Intell Rev 54, 2827–2861 (2021). https://doi.org/10.1007/s10462-020-09914-6
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10462-020-09914-6