{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T15:03:24Z","timestamp":1781535804101,"version":"3.54.5"},"reference-count":83,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2023,4,28]],"date-time":"2023-04-28T00:00:00Z","timestamp":1682640000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,4,28]],"date-time":"2023-04-28T00:00:00Z","timestamp":1682640000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Intell Rev"],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1007\/s10462-023-10493-5","type":"journal-article","created":{"date-parts":[[2023,4,28]],"date-time":"2023-04-28T07:02:33Z","timestamp":1682665353000},"page":"14035-14086","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Machine learning and deep learning techniques for the analysis of heart disease: a systematic literature review, open challenges and future directions"],"prefix":"10.1007","volume":"56","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4309-875X","authenticated-orcid":false,"given":"Megha","family":"Bhushan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akkshat","family":"Pandit","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ayush","family":"Garg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,4,28]]},"reference":[{"issue":"6","key":"10493_CR1","doi-asserted-by":"crossref","first-page":"FSO698","DOI":"10.2144\/fsoa-2020-0206","volume":"7","author":"A Akella","year":"2021","unstructured":"Akella A, Akella S (2021) Machine learning algorithms for predicting coronary artery disease: efforts toward an open-source solution. Future Sci OA 7(6):FSO698","journal-title":"Future Sci OA"},{"key":"10493_CR3","doi-asserted-by":"publisher","first-page":"34938","DOI":"10.1109\/ACCESS.2019.2904800","volume":"7","author":"L Ali","year":"2019","unstructured":"Ali L, Rahman A, Khan A, Zhou M, Javeed A, Khan JA (2019) An automated diagnostic system for heart disease prediction based on \u03c72 statistical model and optimally configured deep neural network. IEEE Access 7:34938\u201334945. https:\/\/doi.org\/10.1109\/ACCESS.2019.2904800","journal-title":"IEEE Access"},{"key":"10493_CR2","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.inffus.2020.06.008","volume":"63","author":"F Ali","year":"2020","unstructured":"Ali F, El-Sappagh S, Riazul Islam SM, Kwak D, Ali A, Imran M, Kwak K-S (2020) A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion. Inf Fusion 63:208\u2013222","journal-title":"Inf Fusion"},{"issue":"1","key":"10493_CR4","doi-asserted-by":"crossref","first-page":"134","DOI":"10.3934\/mbe.2022007","volume":"19","author":"AA Almazroi","year":"2022","unstructured":"Almazroi AA (2022) Survival prediction among heart patients using machine learning techniques. Math Biosci Eng 19(1):134\u2013145","journal-title":"Math Biosci Eng"},{"issue":"24","key":"10493_CR5","doi-asserted-by":"crossref","first-page":"13405","DOI":"10.1007\/s00500-022-07499-6","volume":"26","author":"AM Alqudah","year":"2022","unstructured":"Alqudah AM, Qazan S, Obeidat YM (2022) Deep learning models for detecting respiratory pathologies from raw lung auscultation sounds. Soft Comput 26(24):13405\u201313429","journal-title":"Soft Comput"},{"key":"10493_CR6","doi-asserted-by":"publisher","unstructured":"Arghandabi H, Shams P (2020) A comparative study of machine learning algorithms for the prediction of heart disease. https:\/\/doi.org\/10.22214\/ijraset.2020.32591","DOI":"10.22214\/ijraset.2020.32591"},{"key":"10493_CR7","doi-asserted-by":"publisher","DOI":"10.12720\/jait.13.1.95-99","author":"JCT Arroyo","year":"2022","unstructured":"Arroyo JCT, Delima AJP (2022) An optimized neural network using genetic algorithm for cardiovascular disease prediction. J Adv Inf Technol. https:\/\/doi.org\/10.12720\/jait.13.1.95-99","journal-title":"J Adv Inf Technol"},{"key":"10493_CR9","doi-asserted-by":"crossref","unstructured":"Arya R, Kumar A, Bhushan M (2021) Affect recognition using brain signals: a survey. In: Computational methods and data engineering. Springer, Singapore, pp 529\u2013552","DOI":"10.1007\/978-981-15-7907-3_40"},{"issue":"2","key":"10493_CR8","first-page":"1","volume":"11","author":"R Arya","year":"2022","unstructured":"Arya R, Kumar A, Bhushan M, Samant P (2022) Big five personality traits prediction using brain signals. Int J Fuzzy Syst Appl 11(2):1\u201310","journal-title":"Int J Fuzzy Syst Appl"},{"issue":"2","key":"10493_CR10","doi-asserted-by":"crossref","first-page":"49","DOI":"10.51983\/ajcst-2019.8.2.2141","volume":"8","author":"M Ashraf","year":"2019","unstructured":"Ashraf M, Rizvi MA, Sharma H (2019) Improved heart disease prediction using deep neural network. Asian J Comput Sci Technol 8(2):49\u201354","journal-title":"Asian J Comput Sci Technol"},{"key":"10493_CR11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2021\/8387680","volume":"2021","author":"R Bharti","year":"2021","unstructured":"Bharti R, Khamparia A, Shabaz M, Dhiman G, Pande S, Singh P (2021) Prediction of heart disease using a combination of machine learning and deep learning. Comput Intell Neurosci 2021:1\u201311","journal-title":"Comput Intell Neurosci"},{"issue":"4","key":"10493_CR12","doi-asserted-by":"crossref","first-page":"3125","DOI":"10.22270\/jmpas.V10I4.1395","volume":"10","author":"D Bhattacharyya","year":"2021","unstructured":"Bhattacharyya D, Dinesh Reddy B, Joys Kumari NM, Thirupathi Rao N (2021) Comprehensive analysis on comparison of machine learning and deep learning applications on cardiac arrest. J Med Pharm Allied Sci 10(4):3125\u20133131","journal-title":"J Med Pharm Allied Sci"},{"key":"10493_CR13","doi-asserted-by":"crossref","unstructured":"Biswas R, Beeravolu AR, Karim A, Azam S, Hasan MT, Alam MS, Ghosh P (2021) A robust deep learning based prediction system of heart disease using a combination of five datasets. In: 2021 31st International conference on computer theory and applications (ICCTA), 2021. IEEE, pp 223\u2013228","DOI":"10.1109\/ICCTA54562.2021.9916601"},{"issue":"01","key":"10493_CR14","first-page":"17","volume":"3","author":"JIZ Chen","year":"2021","unstructured":"Chen JIZ, Hengjinda P (2021) Early prediction of coronary artery disease (CAD) by machine learning method\u2014a comparative study. J Artif Intell 3(01):17\u201333","journal-title":"J Artif Intell"},{"issue":"13","key":"10493_CR15","doi-asserted-by":"crossref","first-page":"7979","DOI":"10.1007\/s00521-020-05542-x","volume":"33","author":"S Dami","year":"2021","unstructured":"Dami S, Yahaghizadeh M (2021) Predicting cardiovascular events with deep learning approach in the context of the internet of things. Neural Comput Appl 33(13):7979\u20137996","journal-title":"Neural Comput Appl"},{"key":"10493_CR16","doi-asserted-by":"crossref","first-page":"e825","DOI":"10.7717\/peerj-cs.825","volume":"8","author":"A Darmawahyuni","year":"2022","unstructured":"Darmawahyuni A, Nurmaini S, Rachmatullah MN, Tutuko B, Sapitri AI, Firdaus F, Fansyuri A, Predyansyah A (2022) Deep learning-based electrocardiogram rhythm and beat features for heart abnormality classification. PeerJ Comput Sci 8:e825","journal-title":"PeerJ Comput Sci"},{"key":"10493_CR17","doi-asserted-by":"crossref","first-page":"133034","DOI":"10.1109\/ACCESS.2020.3010511","volume":"8","author":"NL Fitriyani","year":"2020","unstructured":"Fitriyani NL, Syafrudin M, Alfian G, Rhee J (2020) HDPM: an effective heart disease prediction model for a clinical decision support system. IEEE Access 8:133034\u2013133050","journal-title":"IEEE Access"},{"issue":"1","key":"10493_CR18","doi-asserted-by":"crossref","first-page":"012046","DOI":"10.1088\/1757-899X\/1022\/1\/012046","volume":"1022","author":"A Garg","year":"2021","unstructured":"Garg A, Sharma B, Khan R (2021) Heart disease prediction using machine learning techniques. IOP Conf Ser Mater Sci Eng 1022(1):012046","journal-title":"IOP Conf Ser Mater Sci Eng"},{"key":"10493_CR19","doi-asserted-by":"crossref","unstructured":"Ghoniem RM, Shaalan K (2017) FCSR\u2014fuzzy continuous speech recognition approach for identifying laryngeal pathologies using new weighted spectrum features. In: International conference on advanced intelligent systems and informatics, 2017. Springer, Cham, pp 384\u2013395","DOI":"10.1007\/978-3-319-64861-3_36"},{"key":"10493_CR20","doi-asserted-by":"crossref","first-page":"19304","DOI":"10.1109\/ACCESS.2021.3053759","volume":"9","author":"P Ghosh","year":"2021","unstructured":"Ghosh P, Azam S, Jonkman M, Karim A, Javed Mehedi Shamrat FM, Ignatious E, Shultana S, Beeravolu AR, De Boer F (2021) Efficient prediction of cardiovascular disease using machine learning algorithms with relief and LASSO feature selection techniques. IEEE Access 9:19304\u201319326","journal-title":"IEEE Access"},{"issue":"1","key":"10493_CR21","doi-asserted-by":"crossref","first-page":"012010","DOI":"10.1088\/1742-6596\/2161\/1\/012010","volume":"2161","author":"G Gupta","year":"2022","unstructured":"Gupta G, Adarsh U, Subba Reddy NV, Ashwath Rao B (2022a) Comparison of various machine learning approaches uses in heart ailments prediction. J Phys Conf Ser 2161(1):012010","journal-title":"J Phys Conf Ser"},{"issue":"1","key":"10493_CR22","doi-asserted-by":"crossref","first-page":"012013","DOI":"10.1088\/1742-6596\/2161\/1\/012013","volume":"2161","author":"C Gupta","year":"2022","unstructured":"Gupta C, Saha A, Subba Reddy NV, Dinesh Acharya U (2022b) Cardiac disease prediction using supervised machine learning techniques. J Phys Conf Ser 2161(1):012013","journal-title":"J Phys Conf Ser"},{"key":"10493_CR23","doi-asserted-by":"crossref","unstructured":"Ha U, Assana S, Adib F (2020) Contactless seismocardiography via deep learning radars. In: Proceedings of the 26th annual international conference on mobile computing and networking, 2020, pp 1\u201314","DOI":"10.1145\/3372224.3419982"},{"issue":"1","key":"10493_CR24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.31580\/ojst.v4i1.1186","volume":"4","author":"A Hamad","year":"2021","unstructured":"Hamad A, Jasim A (2021) Heart disease diagnosis based on deep learning network. Open J Sci Technol 4(1):1\u20139","journal-title":"Open J Sci Technol"},{"issue":"2","key":"10493_CR25","doi-asserted-by":"crossref","first-page":"1143","DOI":"10.1002\/mrm.27480","volume":"81","author":"A Hauptmann","year":"2019","unstructured":"Hauptmann A, Arridge S, Lucka F, Muthurangu V, Steeden JA (2019) Real-time cardiovascular MR with spatio-temporal artifact suppression using deep learning\u2014proof of concept in congenital heart disease. Magn Reson Med 81(2):1143\u20131156","journal-title":"Magn Reson Med"},{"key":"10493_CR26","doi-asserted-by":"crossref","unstructured":"Hussain S, Nanda SK, Barigidad S, Akhtar S, Suaib M, Ray NK (2021) Novel deep learning architecture for predicting heart disease using CNN. In: 2021 19th OITS international conference on information technology (OCIT), 2021. IEEE, pp 353\u2013357","DOI":"10.1109\/OCIT53463.2021.00076"},{"key":"10493_CR27","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.procs.2020.06.017","volume":"173","author":"R Indrakumari","year":"2020","unstructured":"Indrakumari R, Poongodi T, Jena SR (2020) Heart disease prediction using exploratory data analysis. Procedia Comput Sci 173:130\u2013139","journal-title":"Procedia Comput Sci"},{"key":"10493_CR30","doi-asserted-by":"crossref","first-page":"180235","DOI":"10.1109\/ACCESS.2019.2952107","volume":"7","author":"A Javeed","year":"2019","unstructured":"Javeed A, Zhou S, Yongjian L, Qasim I, Noor A, Nour R (2019) An intelligent learning system based on random search algorithm and optimized random forest model for improved heart disease detection. IEEE Access 7:180235\u2013180243","journal-title":"IEEE Access"},{"key":"10493_CR29","first-page":"1","volume":"2020","author":"A Javeed","year":"2020","unstructured":"Javeed A, Rizvi SS, Zhou S, Riaz R, Khan SU, Kwon SJ (2020) Heart risk failure prediction using a novel feature selection method for feature refinement and neural network for classification. Mob Inf Syst 2020:1\u201311","journal-title":"Mob Inf Syst"},{"key":"10493_CR28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2022\/9288452","volume":"2022","author":"A Javeed","year":"2022","unstructured":"Javeed A, Khan SU, Ali L, Ali S, Imrana Y, Rahman A (2022) Machine learning-based automated diagnostic systems developed for heart failure prediction using different types of data modalities: a systematic review and future directions. Comput Math Methods Med 2022:1\u201330","journal-title":"Comput Math Methods Med"},{"issue":"1","key":"10493_CR31","doi-asserted-by":"crossref","first-page":"012072","DOI":"10.1088\/1757-899X\/1022\/1\/012072","volume":"1022","author":"H Jindal","year":"2021","unstructured":"Jindal H, Agrawal S, Khera R, Jain R, Nagrath P (2021) Heart disease prediction using machine learning algorithms. IOP Conf Ser Mater Sci Eng 1022(1):012072","journal-title":"IOP Conf Ser Mater Sci Eng"},{"issue":"3","key":"10493_CR32","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1007\/s00354-021-00124-4","volume":"39","author":"T Karadeniz","year":"2021","unstructured":"Karadeniz T, Tokdemir G, Mara\u015f HH (2021) Ensemble methods for heart disease prediction. N Gener Comput 39(3):569\u2013581","journal-title":"N Gener Comput"},{"issue":"1","key":"10493_CR33","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/s12553-020-00505-7","volume":"11","author":"R Katarya","year":"2021","unstructured":"Katarya R, Meena SK (2021) Machine learning techniques for heart disease prediction: a comparative study and analysis. Health Technol 11(1):87\u201397","journal-title":"Health Technol"},{"key":"10493_CR34","doi-asserted-by":"crossref","unstructured":"Kavitha M, Gnaneswar G, Dinesh R, Rohith Sai Y, Sai Suraj R (2021) Heart disease prediction using hybrid machine learning model. In: 2021 6th International conference on inventive computation technologies (ICICT), 2021. IEEE, pp 1329\u20131333","DOI":"10.1109\/ICICT50816.2021.9358597"},{"key":"10493_CR35","doi-asserted-by":"publisher","unstructured":"Kedia S, Bhushan M (2022) Prediction of mortality from heart failure using machine learning. In: Proceedings of the 2nd international conference on emerging frontiers in electrical and electronic technologies (ICEFEET), 2022, pp. 1\u20136. https:\/\/doi.org\/10.1109\/ICEFEET51821.2022.9848348","DOI":"10.1109\/ICEFEET51821.2022.9848348"},{"key":"10493_CR37","unstructured":"Kitchenham B (2004) Procedures for performing systematic reviews. Joint Technical Report, Keele University Technical Report TR\/SE-0401 and NICTA Technical Report 0400011T.1. Software Engineering Group, Department of Computer Science, Keele University, UK and Empirical Software Engineering, National ICT Australia Ltd"},{"key":"10493_CR38","unstructured":"Kitchenham B, Charters S (2007) Guidelines for performing systematic literature reviews in software engineering (version 2.3). EBSE Technical Report EBSE-2007-01. Software Engineering Group, School of Computer Science and Mathematics, Keele University, Keele and Department of Computer Science, University of Durham, Durham"},{"issue":"1","key":"10493_CR36","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1016\/j.infsof.2008.09.009","volume":"51","author":"B Kitchenham","year":"2009","unstructured":"Kitchenham B, Brereton P, Budgen D, Turner M, Bailey J, Linkman S (2009) Systematic literature reviews in software engineering\u2014a systematic literature review. Inf Softw Technol 51(1):7\u201315. https:\/\/doi.org\/10.1016\/j.infsof.2008.09.009","journal-title":"Inf Softw Technol"},{"issue":"1","key":"10493_CR39","doi-asserted-by":"crossref","first-page":"371","DOI":"10.3390\/app11010371","volume":"11","author":"M Komatsu","year":"2021","unstructured":"Komatsu M, Sakai A, Komatsu R, Matsuoka R, Yasutomi S, Shozu K, Dozen A et al (2021) Detection of cardiac structural abnormalities in fetal ultrasound videos using deep learning. Appl Sci 11(1):371","journal-title":"Appl Sci"},{"issue":"6","key":"10493_CR40","first-page":"5467","volume":"11","author":"S Krishnan","year":"2021","unstructured":"Krishnan S, Magalingam P, Ibrahim R (2021) Hybrid deep learning model using recurrent neural network and gated recurrent unit for heart disease prediction. Int J Electr Comput Eng 11(6):5467","journal-title":"Int J Electr Comput Eng"},{"issue":"1","key":"10493_CR41","first-page":"855","volume":"71","author":"VDA Kumar","year":"2022","unstructured":"Kumar VDA, Swarup C, Murugan I, Kumar A, Singh KU, Singh T, Dubey R (2022) Prediction of cardiovascular disease using machine learning technique\u2014a modern approach. Comput Mater Contin 71(1):855\u2013869","journal-title":"Comput Mater Contin"},{"key":"10493_CR42","doi-asserted-by":"crossref","first-page":"102474","DOI":"10.1016\/j.bspc.2021.102474","volume":"66","author":"P Li","year":"2021","unstructured":"Li P, Hu Y, Liu Z-P (2021) Prediction of cardiovascular diseases by integrating multi-modal features with machine learning methods. Biomed Signal Process Control 66:102474","journal-title":"Biomed Signal Process Control"},{"issue":"4","key":"10493_CR43","doi-asserted-by":"crossref","first-page":"3409","DOI":"10.1007\/s13369-020-05105-1","volume":"46","author":"A Mehmood","year":"2021","unstructured":"Mehmood A, Iqbal M, Mehmood Z, Irtaza A, Nawaz M, Nazir T, Masood M (2021) Prediction of heart disease using deep convolutional neural networks. Arab J Sci Eng 46(4):3409\u20133422","journal-title":"Arab J Sci Eng"},{"key":"10493_CR44","doi-asserted-by":"crossref","first-page":"100402","DOI":"10.1016\/j.imu.2020.100402","volume":"20","author":"ID Mienye","year":"2020","unstructured":"Mienye ID, Sun Y, Wang Z (2020) An improved ensemble learning approach for the prediction of heart disease risk. Inform Med Unlocked 20:100402","journal-title":"Inform Med Unlocked"},{"key":"10493_CR45","doi-asserted-by":"crossref","first-page":"81542","DOI":"10.1109\/ACCESS.2019.2923707","volume":"7","author":"S Mohan","year":"2019","unstructured":"Mohan S, Thirumalai C, Srivastava G (2019) Effective heart disease prediction using hybrid machine learning techniques. IEEE Access 7:81542\u201381554","journal-title":"IEEE Access"},{"issue":"4","key":"10493_CR46","doi-asserted-by":"crossref","first-page":"854","DOI":"10.4236\/wjet.2018.64057","volume":"6","author":"S Nashif","year":"2018","unstructured":"Nashif S, Raihan MR, Islam MR, Imam MH (2018) Heart disease detection by using machine learning algorithms and a real-time cardiovascular health monitoring system. World J Eng Technol 6(4):854\u2013873","journal-title":"World J Eng Technol"},{"issue":"S1","key":"10493_CR47","first-page":"1","volume":"13","author":"AS Oliver","year":"2021","unstructured":"Oliver AS, Ganesan K, Yuvaraj SA, Jayasankar T, Sikkandar MY, Prakash NB (2021) Accurate prediction of heart disease based on bio system using regressive learning based neural network classifier. J Ambient Intell Humaniz Comput 13(S1):1\u20139","journal-title":"J Ambient Intell Humaniz Comput"},{"issue":"1","key":"10493_CR48","doi-asserted-by":"crossref","first-page":"012009","DOI":"10.1088\/1742-6596\/1817\/1\/012009","volume":"1817","author":"M Pal","year":"2021","unstructured":"Pal M, Parija S (2021) Prediction of heart diseases using random forest. J Phys Conf Ser 1817(1):012009","journal-title":"J Phys Conf Ser"},{"key":"10493_CR49","doi-asserted-by":"crossref","unstructured":"Pal S, Mishra N, Bhushan M, Kholiya PS, Rana M, Negi A (2022) Deep learning techniques for prediction and diagnosis of diabetes mellitus. In: 2022 International mobile and embedded technology conference (MECON), March 2022. IEEE, pp 588\u2013593","DOI":"10.1109\/MECON53876.2022.9752176"},{"key":"10493_CR50","doi-asserted-by":"crossref","first-page":"189503","DOI":"10.1109\/ACCESS.2020.3026214","volume":"8","author":"Y Pan","year":"2020","unstructured":"Pan Y, Fu M, Cheng B, Tao X, Guo J (2020) Enhanced deep learning assisted convolutional neural network for heart disease prediction on the Internet of Medical Things platform. IEEE Access 8:189503\u2013189512","journal-title":"IEEE Access"},{"issue":"19","key":"10493_CR51","doi-asserted-by":"publisher","first-page":"7877","DOI":"10.31838\/jcr.07.19.896","volume":"7","author":"S Pathan","year":"2020","unstructured":"Pathan S, Bhushan M, Bai A (2020) A study on health care using data mining techniques. J Crit Rev 7(19):7877\u20137890. https:\/\/doi.org\/10.31838\/jcr.07.19.896","journal-title":"J Crit Rev"},{"key":"10493_CR52","unstructured":"Patil AH, Sonawane OS, Sopan V (2022) Risk prediction of cardiovascular disease using logistic regression machine learning algorithm. Int Res J Mod Eng Technol Sci 4(1)"},{"key":"10493_CR53","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1038\/s41551-018-0195-0","volume":"2","author":"R Poplin","year":"2018","unstructured":"Poplin R, Varadarajan AV, Blumer K, Liu Y, McConnell MV, Corrado GS, Peng L, Webster DR (2018) Webster Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat Biomed Eng 2:158\u2013164. https:\/\/doi.org\/10.1038\/s41551-018-0195-0","journal-title":"Nat Biomed Eng"},{"issue":"04","key":"10493_CR54","first-page":"659","volume":"9","author":"A Rajdhan","year":"2020","unstructured":"Rajdhan A, Agarwal A, Sai M, Ravi D, Ghuli P (2020) Heart disease prediction using machine learning. Int J Res Technol 9(04):659\u2013662","journal-title":"Int J Res Technol"},{"key":"10493_CR55","unstructured":"Ramirez PM, Uus A, van Poppel MPM, Grigorescu I, Steinweg JK, Lloyd DFA, Pushparajah K, King AP, Deprez M (2022) Automated atlas-based multi-label fetal cardiac vessel segmentation in Congenital Heart Disease. bioRxiv"},{"key":"10493_CR56","doi-asserted-by":"crossref","unstructured":"Rana M, Bhushan M (2022) Advancements in healthcare services using deep learning techniques. In: 2022 International mobile and embedded technology conference (MECON), March 2022. IEEE, pp 157\u2013161","DOI":"10.1109\/MECON53876.2022.9752020"},{"issue":"3","key":"10493_CR57","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1007\/s40860-021-00133-6","volume":"7","author":"P Rani","year":"2021","unstructured":"Rani P, Kumar R, Ahmed NMO, Jain A (2021) A decision support system for heart disease prediction based upon machine learning. J Reliab Intell Environ 7(3):263\u2013275","journal-title":"J Reliab Intell Environ"},{"key":"10493_CR58","doi-asserted-by":"publisher","DOI":"10.32628\/IJSRSET218118","author":"JN Rao","year":"2021","unstructured":"Rao JN, Satya Prasad R (2021) An ensemble deep dynamic algorithm (EDDA) to predict the heart disease. Int J Sci Res Sci Eng Technol. https:\/\/doi.org\/10.32628\/IJSRSET218118","journal-title":"Int J Sci Res Sci Eng Technol"},{"issue":"1","key":"10493_CR59","doi-asserted-by":"crossref","first-page":"1151","DOI":"10.14704\/WEB\/V19I1\/WEB19078","volume":"19","author":"AA Rawi","year":"2022","unstructured":"Rawi AA, Albashir MK, Ahmed AM (2022) Classification and detection of ECG arrhythmia and myocardial infarction using deep learning: a review. Webology 19(1):1151\u20131170","journal-title":"Webology"},{"issue":"1","key":"10493_CR60","doi-asserted-by":"crossref","first-page":"012015","DOI":"10.1088\/1742-6596\/2161\/1\/012015","volume":"2161","author":"VSK Reddy","year":"2022","unstructured":"Reddy VSK, Meghana P, Subba Reddy NV, Ashwath Rao B (2022) Prediction on Cardiovascular disease using Decision tree and Na\u00efve Bayes classifiers. J Phys Conf Ser 2161(1):012015","journal-title":"J Phys Conf Ser"},{"key":"10493_CR61","unstructured":"Revathi Ch, Anjuaravind C (2021) Artificial intelligence tool for heart disease prediction using deep learning CNN. J Eng Sci 12(02):63\u201370"},{"issue":"2","key":"10493_CR62","first-page":"904","volume":"25","author":"PE Rubini","year":"2021","unstructured":"Rubini PE, Subasini CA, Vanitha Katharine A, Kumaresan V, Gowdham Kumar S, Nithya TM (2021) A cardiovascular disease prediction using machine learning algorithms. Ann Rom Soc Cell Biol 25(2):904\u2013912","journal-title":"Ann Rom Soc Cell Biol"},{"issue":"5","key":"10493_CR63","first-page":"601","volume":"34","author":"TK Sajja","year":"2020","unstructured":"Sajja TK, Kalluri HK (2020) A deep learning method for prediction of cardiovascular disease using convolutional neural network. Rev Intell Artif 34(5):601\u2013606","journal-title":"Rev Intell Artif"},{"key":"10493_CR64","doi-asserted-by":"publisher","first-page":"1","DOI":"10.21203\/rs.3.rs-1058279\/v1","volume":"3","author":"S Sandhiya","year":"2022","unstructured":"Sandhiya S, Palani U (2022) An IoT enabled heart disease monitoring system using grey wolf optimization and deep belief network. Res Sq 3:1. https:\/\/doi.org\/10.21203\/rs.3.rs-1058279\/v1","journal-title":"Res Sq"},{"key":"10493_CR65","doi-asserted-by":"crossref","first-page":"135784","DOI":"10.1109\/ACCESS.2020.3007561","volume":"8","author":"SS Sarmah","year":"2020","unstructured":"Sarmah SS (2020) An efficient IoT-based patient monitoring and heart disease prediction system using deep learning modified neural network. IEEE Access 8:135784\u2013135797","journal-title":"IEEE Access"},{"issue":"3","key":"10493_CR66","doi-asserted-by":"crossref","first-page":"2244","DOI":"10.35940\/ijitee.C9009.019320","volume":"9","author":"S Sharma","year":"2020","unstructured":"Sharma S, Parmar M (2020) Heart diseases prediction using deep learning neural network model. Int J Innov Technol Explor Eng 9(3):2244\u20132248","journal-title":"Int J Innov Technol Explor Eng"},{"issue":"10","key":"10493_CR67","first-page":"3648","volume":"12","author":"SI Sherly","year":"2021","unstructured":"Sherly SI (2021) An ensemble based heart disease prediction using gradient boosting decision tree. Turk J Comput Math Educ 12(10):3648\u20133660","journal-title":"Turk J Comput Math Educ"},{"key":"10493_CR68","doi-asserted-by":"crossref","DOI":"10.1016\/j.imu.2021.100655","volume":"26","author":"V Shorewala","year":"2021","unstructured":"Shorewala V (2021) Early detection of coronary heart disease using ensemble techniques. Inform Med Unlocked 26:100655","journal-title":"Inform Med Unlocked"},{"key":"10493_CR69","doi-asserted-by":"crossref","first-page":"36955","DOI":"10.1109\/ACCESS.2021.3063129","volume":"9","author":"SB Shuvo","year":"2021","unstructured":"Shuvo SB, Ali SN, Swapnil SI, Al-Rakhami MS, Gumaei A (2021) CardioXNet: a novel lightweight deep learning framework for cardiovascular disease classification using heart sound recordings. IEEE Access 9:36955\u201336967","journal-title":"IEEE Access"},{"key":"10493_CR70","doi-asserted-by":"publisher","unstructured":"Singh SN, Bhushan M (2022) Smart ECG monitoring and analysis system using machine learning. In: Proceedings of the 2022 IEEE VLSI device circuit and system (VLSI DCS), 2022, pp 304\u2013309. https:\/\/doi.org\/10.1109\/VLSIDCS53788.2022.9811433","DOI":"10.1109\/VLSIDCS53788.2022.9811433"},{"key":"10493_CR71","doi-asserted-by":"crossref","unstructured":"Singh A, Kumar R (2020) Heart disease prediction using machine learning algorithms. In: 2020 International conference on electrical and electronics engineering (ICE3), 2020. IEEE, pp 452\u2013457","DOI":"10.1109\/ICE348803.2020.9122958"},{"key":"10493_CR72","unstructured":"Singh VJ, Bhushan M, Kumar V, Bansal KL (2015) Optimization of segment size assuring application perceived QoS in healthcare. In: Proceedings of the world congress on engineering, 2015, vol 1"},{"issue":"1","key":"10493_CR73","first-page":"257","volume":"23","author":"S Singhal","year":"2018","unstructured":"Singhal S, Kumar H, Passricha V (2018) Prediction of heart disease using CNN. Am Int J Res Sci Technol Eng Math 23(1):257\u2013261","journal-title":"Am Int J Res Sci Technol Eng Math"},{"issue":"5","key":"10493_CR74","doi-asserted-by":"crossref","first-page":"5405","DOI":"10.1007\/s12652-020-02027-6","volume":"12","author":"C Sowmiya","year":"2021","unstructured":"Sowmiya C, Sumitra P (2021) A hybrid approach for mortality prediction for heart patients using ACO-HKNN. J Ambient Intell Humaniz Comput 12(5):5405\u20135412","journal-title":"J Ambient Intell Humaniz Comput"},{"issue":"4","key":"10493_CR75","first-page":"36","volume":"6","author":"A Sridhar","year":"2018","unstructured":"Sridhar A, Kapardhi A (2018) Predicting heart disease using machine learning algorithm. Int Res J Eng Technol 6(4):36\u201338","journal-title":"Int Res J Eng Technol"},{"issue":"5","key":"10493_CR76","first-page":"484","volume":"8","author":"K Subhadra","year":"2019","unstructured":"Subhadra K, Vikas B (2019) Neural network based intelligent system for predicting heart disease. Int J Innov Technol Explor Eng 8(5):484\u2013487","journal-title":"Int J Innov Technol Explor Eng"},{"key":"10493_CR77","doi-asserted-by":"crossref","unstructured":"Sujatha P, Mahalakshmi K (2020) Performance evaluation of supervised machine learning algorithms in prediction of heart disease. In: 2020 IEEE international conference for innovation in technology (INOCON), 2020. IEEE, pp 1\u20137","DOI":"10.1109\/INOCON50539.2020.9298354"},{"issue":"1","key":"10493_CR78","doi-asserted-by":"crossref","first-page":"30","DOI":"10.52810\/TIOT.2021.100035","volume":"1","author":"W Sun","year":"2021","unstructured":"Sun W, Zhang P, Wang Z, Li D (2021) Prediction of cardiovascular diseases based on machine learning. ASP Trans Internet Things 1(1):30\u201335","journal-title":"ASP Trans Internet Things"},{"key":"10493_CR79","doi-asserted-by":"publisher","unstructured":"Tomov S, Tomov S (2021) A novel deep learning approach to improving heart disease diagnosis. https:\/\/doi.org\/10.13140\/RG.2.2.11232.12806","DOI":"10.13140\/RG.2.2.11232.12806"},{"key":"10493_CR80","doi-asserted-by":"publisher","DOI":"10.51220\/jmr.v16i3.19","author":"K Verma","year":"2021","unstructured":"Verma K, Bartwal AS, Thapliyal MP (2021) A genetic algorithm based hybrid deep learning approach for heart disease prediction. J Mt Res. https:\/\/doi.org\/10.51220\/jmr.v16i3.19","journal-title":"J Mt Res"},{"key":"10493_CR81","doi-asserted-by":"publisher","DOI":"10.1016\/j.matpr.2021.01.570","author":"SF Waris","year":"2021","unstructured":"Waris SF, Koteeswaran S (2021) Heart disease early prediction using a novel machine learning method called improved K-means neighbor classifier in Python. Mater Today Proc. https:\/\/doi.org\/10.1016\/j.matpr.2021.01.570","journal-title":"Mater Today Proc"},{"issue":"1","key":"10493_CR82","doi-asserted-by":"crossref","first-page":"40","DOI":"10.4018\/IJBDAH.20210101.oa4","volume":"6","author":"DC Yadav","year":"2021","unstructured":"Yadav DC, Pal S (2021) Analysis of heart disease using parallel and sequential ensemble methods with feature selection techniques: heart disease prediction. Int J Big Data Anal Healthc 6(1):40\u201356","journal-title":"Int J Big Data Anal Healthc"},{"issue":"1","key":"10493_CR83","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12911-021-01436-7","volume":"21","author":"Q Zhenya","year":"2021","unstructured":"Zhenya Q, Zhang Z (2021) A hybrid cost-sensitive ensemble for heart disease prediction. BMC Med Inform Decis Mak 21(1):1\u201318","journal-title":"BMC Med Inform Decis Mak"}],"container-title":["Artificial Intelligence Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-023-10493-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10462-023-10493-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-023-10493-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,5]],"date-time":"2023-10-05T10:10:14Z","timestamp":1696500614000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10462-023-10493-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,28]]},"references-count":83,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["10493"],"URL":"https:\/\/doi.org\/10.1007\/s10462-023-10493-5","relation":{},"ISSN":["0269-2821","1573-7462"],"issn-type":[{"value":"0269-2821","type":"print"},{"value":"1573-7462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,28]]},"assertion":[{"value":"17 April 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 April 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}