Abstract
Coronary artery disease (CAD) is the most prominent disease that is responsible for increasing mortality and morbidity rate from past few decades. Early and accurate detection of CAD (a type of cardiovascular diseases) is among the most pressing needs of society. In this research work, experiments have been carried out with Cleveland dataset in four phases such as (i) single classifiers, (ii) boosted stacking nested ensemble, (iii) boosted voting nested ensemble, and (iv) boosted stacked voting nested ensemble. A generalized framework NestEn_SmVn has been proposed for designed nested ensemble models (phases ii to iv above). The proposed framework (NestEn_SmVn) using boosted stacked voting nested ensemble learning techniques having model (ID EID3-GID6) designed with adaptive boosting and Bayesian network as base-classifiers along with SMO and LMT as meta learners that achieved an highest accuracy of 98.68% with F-measure and ROC values of 98.70 and 99.00% respectively. The best proposed model (ID EID3-GID6) from nested ensemble (phase iv) using proposed framework (NestEn_SmVn) has outperformed all other models from phases (i-iv) and all previous works. Our proposed framework can support the clinical decision system and is able to replace previous CAD diagnostic techniques.
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References
Abdar M (2015) Using decision trees in data mining for predicting factors influencing of heart disease. Carpathian J Electr Comp Eng 8(2):31–36
Abdar M, Yen NY (2017) Design of a universal user model for dynamic crowd preference sensing and decision-making behavior analysis. IEEE Access. https://doi.org/10.1109/ACCESS.2017.2735242
Abdar M, Ksiazek W, Acharya UR, Tan RS, Makarenkov V, Pławiak P (2019a) A new machine learning technique for an accurate diagnosis of coronary artery disease. Comput Methods Programs Biomed. https://doi.org/10.1016/j.cmpb.2019.104992
Abdar M, Acharya UR, Sarrafzadegan N, Makarenkov V (2019b) NE-nu-SVC: a new nested ensemble clinical decision support system for effective diagnosis of coronary artery disease. IEEE Access. https://doi.org/10.1109/ACCESS.2019.2953920
Akgül M, Sönmez ÖE, Özcan T (2019) Diagnosis of heart disease using an intelligent method: a hybrid ann–GA approach. In: Kahraman C, Cebi S, Cevik OS, Oztaysi B, Tolga A, Sari I (eds) Intelligent and fuzzy techniques in big data analytics and decision making INFUS 2019. Advances in intelligent systems and computing, vol 1029. Springer, Cham, pp 1250–1257 (10.1007/978-3-030-23756-1_147)
Ali L, Niamat A, Khan JA, Golilarz NA, Xingzhong X (2019) An expert system based on optimized stacked support vector machines for effective diagnosis of heart disease. IEEE Access 4:2169–3536. https://doi.org/10.1109/ACCESS.2019.2909969
Alizadehsani R, Hosseini MJ, Khosravi A, Khozeimeh F, Roshanzamir M, Sarrafzadegan N et al (2018) Non-invasive detection of coronary artery disease in high-risk patients based on the stenosis prediction of separate coronary arteries. Comput Methods Programs Biomed 162:119–127. https://doi.org/10.1016/j.cmpb.2018.05.009
Alizadehsani R, Roshanzamir M, Abdar M, Beykikhoshk A, Zangooei MH, Khosravi A et al (2019a) Model uncertainty quantification for diagnosis of each main coronary artery stenosis. Soft Comput. https://doi.org/10.1007/s00500-019-04531-0
Alizadehsani R, Abdar M, Roshanzamir M, Khosravi A, Kebria PM, Khozeimeh F et al (2019b) Machine learning-based coronary artery disease diagnosis: a comprehensive review. Comput Biol Med. https://doi.org/10.1016/j.compbiomed.2019.103346
Amin MS, Chiam YK, Varathan KD (2018) Identification of significant features and data mining techniques in predicting heart disease. Telematics Inform 36:82–93. https://doi.org/10.1016/j.tele.2018.11.007
Amma NGB (2012) Cardiovascular disease prediction system using genetic algorithm and neural network. In: International conference on computing, communication and applications, IEEE, pp 2325–6001. https://doi.org/10.1109/ICCCA.2012.6179185
Amma NGB (2013) An intelligent approach based on principal component analysis and adaptive neuro fuzzy inference system for predicting the risk of cardiovascular diseases. In: Fifth international conference on advanced computing (ICoAC), Chennai pp 241–245. https://doi.org/10.1109/ICoAC.2013.6921957
Anooj PKN (2011) Clinical decision support system: risk level prediction of heart disease using weighted fuzzy rules and decision tree rules. Open Comput Sci 1(4):482–498. https://doi.org/10.2478/s13537-011-0032-y
Anooj PK (2012) Clinical decision support system: Risk level prediction of heart disease using weighted fuzzy rules. J King Saud Univ Comp Info Sci 24:27–40. https://doi.org/10.1016/j.jksuci.2011.09.002
Aouabed Z, Abdar M, Tahiri N, Gareau JC, Makarenkov V (2019) A novel effective ensemble model for early detection of coronary artery disease. In: Innovation in information systems and technologies to support learning research. EMENA-ISTL 2019. Learning and analytics in intelligent systems, vol 7. Springer, Cham, pp 480-489. https://doi.org/10.1007/978-3-030-36778-7_53
Arabasadi Z, Alizadehsani R, Roshanzamir M, Moosaei H, Yarifard AA (2017) Computer aided decision making for heart disease detection using hybrid neural network-Genetic algorithm. Comput Methods Programs Biomed 141:19–26. https://doi.org/10.1016/j.cmpb.2017.01.004
Arshad A, Riaz S, Jiao L (2019) Semi-supervised deep fuzzy c-mean clustering for imbalanced multi-class classification. IEEE Access 7(1):28100–28112. https://doi.org/10.1109/ACCESS.2019.2901860
Babič F, Olejár J, Vantová Z, Paralič J (2017) Predictive and descriptive analysis for heart disease diagnosis. In: Federated conference on computer science and information systems (FedCSIS), pp 155–163. https://doi.org/10.15439/2017F219
Beckel C, Sadamori L, Santini S (2013) Automatic socio-economic classification of households using electricity consumption data. In: Proceedings of the fourth international conference on future energy systems, e- Energy'13, pp 7586. https://doi.org/10.1145/2487166.2487175
Bialy RE, Salamay MA, Karam OH, Khalifa ME (2015) Feature Analysis of Coronary Artery Heart Disease Data Sets. Proc Comput Sci 65:459–468. https://doi.org/10.1016/j.procs.2015.09.132
Burse K, Kirar VPS, Burse A, Burse R (2019) Various preprocessing methods for neural network based heart disease prediction. In: Tiwari S, Trivedi M, Mishra K, Misra A, Kumar K (eds) Smart innovations in communication and computational sciences advances in intelligent systems and computing, vol 851. Springer, Singapore, pp 55–65 (10.1007/978-981-13-2414-7_6)
Chauhan S, Aeri BT (2015) The rising incidence of cardiovascular diseases in India: assessing its economic impact. J Prev Cardiol 4(4):735–740
Das R, Turkoglu I, Sengur A (2009) Effective diagnosis of heart disease through neural networks ensembles. Expert Syst Appl 36:7675–7680. https://doi.org/10.1016/j.eswa.2008.09.013
Detrano R, Janosi A, Steinbrunn W et al (1989) International application of a new probability algorithm for the diagnosis of coronary artery disease. Amer J Cardiol 64:304–310
Dua D, Graff C (2019) UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science. [https://archive.ics.uci.edu/ml/index.php]. Accessed 27 Feb 2020
Elsayad A, Fakhr M (2015) Diagnosis of cardiovascular diseases with bayesian classifiers. J Comput Sci. 11(2):274–282. https://doi.org/10.3844/jcssp.2015.274.282
Fida B, Nazir M, Naveed N, Akram S (2011) Heart disease classification ensemble optimization using genetic algorithm. IEEE. https://doi.org/10.1109/INMIC.2011.6151471
Frank E, Hall M, Holmes G, Kirkby R, Pfahringer B, Witten IH et al (2010) Weka-A machine learning workbench for data mining. In: Maimon O, Rokach L (eds) Data mining and knowledge discovery handbook. Springer, Boston, pp 1269–1277 (10.1007/978-0-387-09823-4_66)
Gokulnath CB, Shantharajah SP (2018) An optimized feature selection based on genetic approach and support vector machine for heart disease. Clust Comput 22:14777–14787. https://doi.org/10.1007/s10586-018-2416-4
González OL, Trinidad JFM, Ochoa JAC, Borroto MG (2016) Study of the impact of resampling methods for contrast pattern based classifiers in imbalanced databases. Neurocomputing 175:935–947. https://doi.org/10.1016/j.neucom.2015.04.120
Haq AU, Li JP, Memon MH, Nazir S, Sun R (2018) A hybrid intelligent system framework for the prediction of heart disease using machine learning algorithms. Hindawi Mob Info Syst. https://doi.org/10.1155/2018/3860146
Hu Z, Tang J, Wang Z, Zhang K, Zhang L, Sun Q (2018) Deep learning for image-based cancer detection and diagnosis—a survey. Pattern Recogn 83:134–149. https://doi.org/10.1016/j.patcog.2018.05.014
Kahramanli H, Allahverdi N (2008) Design of a hybrid system for the diabetes and heart diseases. Expert Syst Appl 35:82–89. https://doi.org/10.1016/j.eswa.2007.06.004
Karayılan T, Kılıç Ö (2017) Prediction of heart disease using neural network. In: International conference on computer science and engineering (UBMK), Antalya, IEEE pp 719–723. https://doi.org/10.1109/UBMK.2017.8093512
Kavitha R, Kannan E (2016) An efficient framework for heart disease classification using feature extraction and feature selection technique in data mining. ICETETS. https://doi.org/10.1109/ICETETS.2016.7603000
Khan Y, Qamar U, Asad M, Zeb B (2020) Applying feature selection and weight optimization techniques to enhance artificial neural network for heart disease diagnosis. Adv Intell Syst Comput 1037:340–351. https://doi.org/10.1007/978-3-030-29516-5_26
Kouser RR, Manikandan T, Kumar VV (2018) Heart disease prediction system using artificial neural network, radial basis function and case based reasoning. J Comput Theor Nanosci 15:2810–2817. https://doi.org/10.1166/jctn.2018.7543
Latha CBC, Jeeva SC (2019) Improving the accuracy of prediction of heart disease risk based on ensemble classification techniques. IMU 16:100203. https://doi.org/10.1016/j.imu.2019.100203
Liu X, Wang X, Su Q, Zhang M, Zhu Y, Wang Q et al (2017) A hybrid classification system for heart disease diagnosis based on the RFRS method. Comput Math Methods Med. https://doi.org/10.1155/2017/8272091
Mackay J, Mensah GA (2004) The atlas of heart disease and stroke. World Health Organization, ISSN 9241562768
Mukherji D, Padalia N (2013) Performance analysis of rule based algorithms applied to a cardiovascular dataset. IJCT. 12(2):2277–3061. https://doi.org/10.24297/ijct.v12i2.3319
Paul AK, Shill PC, Rabin MR, Akhand MA (2016) Genetic algorithm based fuzzy decision support system for the diagnosis of heart disease. In: 5th international conference on informatics, electronics and vision (ICIEV) pp 145–150. https://doi.org/10.1109/ICIEV.2016.7759984
Paul AK, Shill PC, Rabin MRI, Murase K (2017) Adaptive weighted fuzzy rule-based system for the risk level assessment of heart disease. Appl Intell 48:1739–1756. https://doi.org/10.1007/s10489-017-1037-6
Polat K, Günes S (2009) A new feature selection method on classification of medical datasets: Kernel F-score feature selection. Expert Syst Appl 36(7):10367–10373. https://doi.org/10.1016/j.eswa.2009.01.041
Polat K, Günes S, Tosun S (2006) Diagnosis of heart disease using artificial immune recognition system and fuzzy weighted pre-processing. Pattern Recogn 39:2186–2193. https://doi.org/10.1016/j.patcog.2006.05.028
Polat K, Sahan S, Günes S (2007) Automatic detection of heart disease using an artificial immune recognition system (AIRS) with fuzzy resource allocation mechanism and k-nn (nearest neighbour) based weighting preprocessing. Expert Syst Appl 32:625–631. https://doi.org/10.1016/j.eswa.2006.01.027
Quinlan JR (1996) Improved use of continuous attributes in C4.5. JAIR 4:77–90. https://doi.org/10.1613/jair.279
Rajab W, Rajab S, Sharma V (2018) Kernel FCM-based ANFIS approach to heart disease prediction. In: Emerging trends in expert applications and security. Advances in intelligent systems and computing, vol 841. Springer, Singapore, pp 643-650. https://doi.org/10.1007/978-981-13-2285-3_75
Sarkar BK (2019) Hybrid model for prediction of heart disease. Soft Comput 24:1903–1925. https://doi.org/10.1007/s00500-019-04022-2
Shastri S, Mansotra V (2019) KDD-based decision making: a conceptual framework model for maternal health and child immunization databases. In: Bhatia SK, Tiwari S, Mishra KK, Trivedi MC (eds) Advances in computer communication and computational sciences advances in intelligent systems and computing. Springer, Singapore, p 924 (10.1007/978-981-13-6861-5_21)
Shastri S, Singh K, Kumar S, Kour P, Mansotra V (2020) Time series forecasting of Covid-19 using deep learning models: India-USA comparative case study. Chaos Solitons Fractals 140:110227. https://doi.org/10.1016/j.chaos.2020.110227
Shastri S, Kour P, Kumar S et al (2020) A nested stacking ensemble model for predicting districts with high and low maternal mortality ratio (MMR) in India. Int J Inf Technol. https://doi.org/10.1007/s41870-020-00560-3
Shastri S, Kour P, Kumar S et al (2021) GBoost: a novel grading-adaboost ensemble approach for automatic identification of erythemato-squamous disease. Int J Inf Technol. https://doi.org/10.1007/s41870-020-00589-4
Shastri S, Singh K, Kumar S et al (2021) Deep-LSTM ensemble framework to forecast Covid-19: an insight to the global pandemic. Int J Inf Technol. https://doi.org/10.1007/s41870-020-00571-0
Shi B, Chen Y, Zhang P, Smith CD, Liu J (2017) Nonlinear feature transformation and deep fusion for alzheimer’s disease staging analysis. Pattern Recogn 63:487–498. https://doi.org/10.1016/j.patcog.2016.09.032
Shouman M, Turner T, Stocker R (2011) Using decision tree for diagnosing heart disease patients. In: Proceedings of the 9th australasian data mining conference (AusDM'11), Ballarat, Australia
Shylaja S, Muralidharan R (2019) Hybrid SVM-ann classifier is used for heart disease prediction system. IJEDR 7(3):2321–9939
Soni J, Ansari U, Sharma D, Soni S (2011) Predictive data mining for medical diagnosis: an overview of heart disease prediction. IJCA 17(8):0975–8887. https://doi.org/10.5120/2237-2860
Soni J, Ansari U, Sharma D (2011) Predictive data mining for medical diagnosis: an overview of heart disease prediction. Int J Comput Appl. https://doi.org/10.5120/2237-2860
Srinivas K, Rao GR, Govardhan A (2014) Rough-fuzzy classifier: a system to predict the heart disease by blending two different set theories. Arab J Sci Eng 39:2857–2868. https://doi.org/10.1007/s13369-013-0934-1
Sun G, Cong Y, Xu X (2018) Active lifelong learning with "watchdog". In: Proceedings of the AAAI conference on artificial intelligence 32(1)
Sun G, Cong Y, Hou D, Fan H, Xu X, Yu H (2017) Joint household characteristic prediction via smart meter data. IEEE Trans Smart Grid 10(2):1834–1844. https://doi.org/10.1109/TSG.2017.2778428
Sun G, Jiang C, Cheng P, Liu Y, Wang X, Fu Y et al (2018) Short-term wind power forecasts by a synthetical similar time series data mining method. Renew Energy 115:575–584
Terrada O, Cherradi B, Raihani A, Bouattane O (2019) Classification and prediction of atherosclerosis diseases using machine learning algorithms. In: 5th International conference on optimization and applications (ICOA), Kenitra, Morocco, IEEE, pp 1–5. https://doi.org/10.1109/ICOA.2019.8727688
Thirugnanam M, Mukherji D, Padalia N, Naidu A (2013) A heart disease prediction model using SVM-Decision Trees-Logistic Regression (SDL). Int J Comput Appl 68(16):0975–8887. https://doi.org/10.5120/11662-7250
Tu MC, Shin D (2009) Effective diagnosis of heart disease through bagging approach. In: 2nd International conference on biomedical engineering and informatics, IEEE. https://doi.org/10.1109/BMEI.2009.5301650
Uyara K, İlhan A (2017) Diagnosis of heart disease using genetic algorithm based trained recurrent fuzzy neural networks. Proc Comput Sci 120:588–593. https://doi.org/10.1016/j.procs.2017.11.283
Vasighia M, Zahraeib A, Bagherib S, Vafaeimanesh J (2013) Diagnosis of coronary heart disease based on Hnmr spectra of human blood plasma using genetic algorithm-based feature selection. J Chemometrics. https://doi.org/10.1002/cem.2517
Verma L, Srivastava S, Negi PC (2016) A hybrid data mining model to predict coronary artery disease cases using non-invasive clinical data. J Med Syst 40:178. https://doi.org/10.1007/s10916-016-0536-z
Vijaya K, Nehemiah HK, Kannan A, Bhuvaneswari NG (2010) Fuzzy neuro genetic approach for predicting the risk of cardiovascular diseases. IJDMMM 2(4):388–402. https://doi.org/10.1504/IJDMMM.2010.035565
Vijiyarani S, Sudha S (2013) An Efficient classification tree technique for heart disease prediction. In: IJCA proceedings on international conference on research trends in computer technologies (ICRTCT). Proceedings published in International Journal of Computer Applications (IJCA) 0975–8887
Wang Y, Kung L, Wang WYC, Cegielski CG (2018) An integrated big data analytics-enabled transformation model: application to health care. Info Manage 55(1):64–79
Xu J, Murphy SL, Kochanek KD, Bastian B, Arias E (2017) Deaths: final data for 2015. Natl Vital Stat Rep 66(6):1–75
Yang H-H, Huang M-L, Lai C-M, Jin J-R (2018) An approach combining data mining and control charts-based model for fault detection in wind turbines. Renew Energy 115:808–816
Yeh J-Y, Wu T-H, Tsao C-W (2011) Using data mining techniques to predict hospitalization of hemodialysis patients. Decis Support Syst 50:439–448. https://doi.org/10.1016/j.dss.2010.11.001
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SS carried out the methodology and Conceptualization, KS carried out experimentation and writing original draft, SK carried out experimentation, review and editing, PK carried out conceptualization and investigation, VM carried out project administration and mentorship. All authors read and approved the final manuscript.
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Shastri, S., Singh, K., Kumar, S. et al. NestEn_SmVn: boosted nested ensemble multiplexing to diagnose coronary artery disease. Evolving Systems 13, 281–295 (2022). https://doi.org/10.1007/s12530-021-09384-3
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DOI: https://doi.org/10.1007/s12530-021-09384-3