{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T11:20:39Z","timestamp":1779880839184,"version":"3.53.1"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"S5","license":[{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,11,8]],"date-time":"2021-11-08T00:00:00Z","timestamp":1636329600000},"content-version":"vor","delay-in-days":7,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["MOST 108-2221-E-037-007"],"award-info":[{"award-number":["MOST 108-2221-E-037-007"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["MOST 109-2221-E-037-005"],"award-info":[{"award-number":["MOST 109-2221-E-037-005"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Sun Yat-Sen University-Kaohsiung Medical University","award":["NSYSUKMU 108-P006"],"award-info":[{"award-number":["NSYSUKMU 108-P006"]}]},{"DOI":"10.13039\/100010002","name":"Ministry of Education","doi-asserted-by":"publisher","award":["Intelligent Manufacturing Research Center"],"award-info":[{"award-number":["Intelligent Manufacturing Research Center"]}],"id":[{"id":"10.13039\/100010002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2021,11]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Atrial fibrillation is a paroxysmal heart disease without any obvious symptoms for most people during the onset. The electrocardiogram (ECG) at the time other than the onset of this disease is not significantly different from that of normal people, which makes it difficult to detect and diagnose. However, if atrial fibrillation is not detected and treated early, it tends to worsen the condition and increase the possibility of stroke. In this paper, P-wave morphology parameters and heart rate variability feature parameters were simultaneously extracted from the ECG. A total of 31 parameters were used as input variables to perform the modeling of artificial intelligence ensemble learning model.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>This paper applied three artificial intelligence ensemble learning methods, namely Bagging ensemble learning method, AdaBoost ensemble learning method, and Stacking ensemble learning method. The prediction results of these three artificial intelligence ensemble learning methods were compared. As a result of the comparison, the Stacking ensemble learning method combined with various models finally obtained the best prediction effect with the accuracy of 92%, sensitivity of 88%, specificity of 96%, positive predictive value of 95.7%, negative predictive value of 88.9%, F1 score of 0.9231 and area under receiver operating characteristic curve value of 0.911.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>In feature extraction, this paper combined P-wave morphology parameters and heart rate variability parameters as input parameters for model training, and validated the value of the proposed parameters combination for the improvement of the model\u2019s predicting effect. In the calculation of the P-wave morphology parameters, the hybrid Taguchi-genetic algorithm was used to obtain more accurate Gaussian function fitting parameters. The prediction model was trained using the Stacking ensemble learning method, so that the model accuracy had better results, which can further improve the early prediction of atrial fibrillation.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12859-021-04000-2","type":"journal-article","created":{"date-parts":[[2021,11,8]],"date-time":"2021-11-08T16:02:43Z","timestamp":1636387363000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Application of artificial intelligence ensemble learning model in early prediction of atrial fibrillation"],"prefix":"10.1186","volume":"22","author":[{"given":"Cai","family":"Wu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maxwell","family":"Hwang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tian-Hsiang","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yen-Ming J.","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiu-Jen","family":"Chang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tsung-Han","family":"Ho","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kao-Shing","family":"Hwang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6194-0563","authenticated-orcid":false,"given":"Wen-Hsien","family":"Ho","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,11,8]]},"reference":[{"issue":"7","key":"4000_CR1","first-page":"e257","volume":"114","author":"V Fuster","year":"2006","unstructured":"Fuster V, Ryd\u00e9n LE, Cannom DS, Crijns HJ, Curtis AB, Ellenbogen KA, Halperin JL, Le Heuzey JY, Kay GN, Lowe JE. ACC\/AHA\/ESC 2006 guidelines for the management of patients with atrial fibrillation. Circulation. 2006;114(7):e257\u2013354.","journal-title":"Circulation"},{"issue":"18","key":"4000_CR2","doi-asserted-by":"publisher","first-page":"2370","DOI":"10.1001\/jama.285.18.2370","volume":"285","author":"AS Go","year":"2001","unstructured":"Go AS, Hylek EM, Phillips KA, Chang Y, Henault LE, Selby JV, Singer DE. Prevalence of diagnosed atrial fibrillation in adults: national implications for rhythm management and stroke prevention: the AnTicoagulation and risk factors in atrial fibrillation (ATRIA) Study. JAMA. 2001;285(18):2370\u20135.","journal-title":"JAMA"},{"issue":"11","key":"4000_CR3","doi-asserted-by":"publisher","first-page":"840","DOI":"10.1001\/jama.1994.03510350050036","volume":"271","author":"EJ Benjamin","year":"1994","unstructured":"Benjamin EJ, Levy D, Vaziri SM, D\u2019agostino RB, Belanger AJ, Wolf PA. Independent risk factors for atrial fibrillation in a population-based cohort: the Framingham heart study. JAMA. 1994;271(11):840\u20134.","journal-title":"JAMA"},{"issue":"3","key":"4000_CR4","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.ahj.2004.09.053","volume":"149","author":"CR Kerr","year":"2005","unstructured":"Kerr CR, Humphries KH, Talajic M, Klein GJ, Connolly SJ, Green M, Boone J, Sheldon R, Dorian P, Newman D. Progression to chronic atrial fibrillation after the initial diagnosis of paroxysmal atrial fibrillation: results from the Canadian registry of atrial fibrillation. Am Heart J. 2005;149(3):489\u201396.","journal-title":"Am Heart J"},{"issue":"1","key":"4000_CR5","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1161\/01.CIR.89.1.224","volume":"89","author":"RL Page","year":"1994","unstructured":"Page RL, Wilkinson WE, Clair WK, McCarthy EA, Pritchett EL. Asymptomatic arrhythmias in patients with symptomatic paroxysmal atrial fibrillation and paroxysmal supraventricular tachycardia. Circulation. 1994;89(1):224\u20137.","journal-title":"Circulation"},{"key":"4000_CR6","doi-asserted-by":"publisher","first-page":"208","DOI":"10.3389\/fphys.2018.00208","volume":"9","author":"GR R\u00edos-Mu\u00f1oz","year":"2018","unstructured":"R\u00edos-Mu\u00f1oz GR, Arenal \u00c1, Art\u00e9s-Rodr\u00edguez A. Real-time rotational activity detection in atrial fibrillation. Front Physiol. 2018;9:208.","journal-title":"Front Physiol"},{"issue":"11","key":"4000_CR7","doi-asserted-by":"publisher","first-page":"981","DOI":"10.1056\/NEJMp1714229","volume":"378","author":"DS Char","year":"2018","unstructured":"Char DS, Shah NH, Magnus D. Implementing machine learning in health care-addressing ethical challenges. N Engl J Med. 2018;378(11):981.","journal-title":"N Engl J Med"},{"key":"4000_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.tube.2017.09.006","volume":"108","author":"P Dande","year":"2018","unstructured":"Dande P, Samant P. Acquaintance to artificial neural networks and use of artificial intelligence as a diagnostic tool for tuberculosis: a review. Tuberculosis. 2018;108:1\u20139.","journal-title":"Tuberculosis"},{"key":"4000_CR9","volume-title":"Advances in knowledge discovery and data mining","author":"UM Fayyad","year":"1996","unstructured":"Fayyad UM, Piatetsky-Shapiro G, Smyth P, Uthurusamy R. Advances in knowledge discovery and data mining. Cambridge: AAAI press\/MIT press; 1996."},{"key":"4000_CR10","doi-asserted-by":"crossref","unstructured":"Dietterich TG. Ensemble methods in machine learning. International workshop on multiple classifier systems. 2000; 1\u201315.","DOI":"10.1007\/3-540-45014-9_1"},{"key":"4000_CR11","first-page":"1","volume":"13","author":"TT Wong","year":"2019","unstructured":"Wong TT, Yeh SJ. Weighted random forests for evaluating financial credit risk. Proc Eng Technol Innov. 2019;13:1\u20139.","journal-title":"Proc Eng Technol Innov"},{"key":"4000_CR12","doi-asserted-by":"publisher","first-page":"012074","DOI":"10.1088\/1742-6596\/1229\/1\/012074","volume":"1229","author":"Y Zhang","year":"2019","unstructured":"Zhang Y, Zhu J. Atrial fibrillation detection based on EEMD and XGBoost. J Phys Conf Ser. 2019;1229:012074.","journal-title":"J Phys Conf Ser"},{"key":"4000_CR13","first-page":"1","volume":"45","author":"R Firoozabadi","year":"2018","unstructured":"Firoozabadi R, Gregg RE, Babaeizadeh S. P-wave analysis in atrial fibrillation detection using a neural network clustering algorithm. Comput Cardiol Conf. 2018;45:1\u20134.","journal-title":"Comput Cardiol Conf"},{"key":"4000_CR14","first-page":"1","volume":"44","author":"M Zabihi","year":"2017","unstructured":"Zabihi M, Rad AB, Katsaggelos AK, Kiranyaz S, Narkilahti S, Gabbouj M. Detection of atrial fibrillation in ECG hand-held devices using a random forest classifier. Comput Cardiol Conf. 2017;44:1\u20134.","journal-title":"Comput Cardiol Conf"},{"key":"4000_CR15","first-page":"113","volume":"28","author":"GB Moody","year":"2001","unstructured":"Moody GB, Goldberger AL, McClennen S, Swiryn SP. Predicting the onset of paroxysmal atrial fibrillation: the computers in cardiology challenge 2001. Comput Cardiol Conf. 2001;28:113\u20136.","journal-title":"Comput Cardiol Conf"},{"key":"4000_CR16","unstructured":"Sahoo SK, Lu W, Teddy SD, Kim D, Feng M. Detection of atrial fibrillation from non-episodic ECG data: a review of methods. In: Annual international conference of the IEEE engineering in medicine and biology society. 2011."},{"key":"4000_CR17","first-page":"99CH37004","volume":"26","author":"J Couderc","year":"1999","unstructured":"Couderc J, Fischer S, Costello A, Daubert J, Konecki J, Zareba W. Wavelet analysis of spatial dispersion of P-wave morphology in patients converted from atrial fibrillation. Comput Cardiol Conf. 1999;26:99CH37004.","journal-title":"Comput Cardiol Conf"},{"issue":"4","key":"4000_CR18","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1109\/10.915704","volume":"48","author":"J Carlson","year":"2001","unstructured":"Carlson J, Johansson R, Olsson SB. Classification of electrocardiographic P-wave morphology. IEEE Trans Biomed Eng. 2001;48(4):401\u20135.","journal-title":"IEEE Trans Biomed Eng"},{"issue":"4","key":"4000_CR19","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1109\/TBME.2006.890134","volume":"54","author":"F Censi","year":"2007","unstructured":"Censi F, Calcagnini G, Ricci C, Ricci RP, Santini M, Grammatico A, Bartolini P. P-wave morphology assessment by a gaussian functions-based model in atrial fibrillation patients. IEEE Trans Biomed Eng. 2007;54(4):663\u201372.","journal-title":"IEEE Trans Biomed Eng"},{"issue":"16","key":"4000_CR20","doi-asserted-by":"publisher","first-page":"1775","DOI":"10.1080\/10255842.2014.964219","volume":"18","author":"A Mart\u00ednez","year":"2015","unstructured":"Mart\u00ednez A, Alcaraz R, Rieta JJ. Gaussian modeling of the P-wave morphology time course applied to anticipate paroxysmal atrial fibrillation. Comput Methods Biomech Biomed Eng. 2015;18(16):1775\u201384.","journal-title":"Comput Methods Biomech Biomed Eng"},{"issue":"4","key":"4000_CR21","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1109\/TEVC.2004.826895","volume":"8","author":"JT Tsai","year":"2004","unstructured":"Tsai JT, Liu TK, Chou JH. Hybrid Taguchi-genetic algorithm for global numerical optimization. IEEE Trans Evol Comput. 2004;8(4):365\u201377.","journal-title":"IEEE Trans Evol Comput"},{"key":"4000_CR22","doi-asserted-by":"publisher","first-page":"6319","DOI":"10.1016\/j.eswa.2010.11.110","volume":"38","author":"WH Ho","year":"2011","unstructured":"Ho WH, Chang CS. Genetic-algorithm-based artificial neural network modeling for platelet transfusion requirements on acute myeloblastic leukemia patients. Expert Syst Appl. 2011;38:6319\u201323.","journal-title":"Expert Syst Appl"},{"key":"4000_CR23","doi-asserted-by":"publisher","first-page":"13050","DOI":"10.1016\/j.eswa.2011.04.109","volume":"38","author":"WH Ho","year":"2011","unstructured":"Ho WH, Chen JX, Lee IN, Su HC. An ANFIS-based model for predicting adequacy of vancomycin regimen using improved genetic algorithm. Expert Syst Appl. 2011;38:13050\u20136.","journal-title":"Expert Syst Appl"},{"key":"4000_CR24","doi-asserted-by":"publisher","first-page":"2304","DOI":"10.1109\/ACCESS.2016.2569537","volume":"4","author":"WH Ho","year":"2016","unstructured":"Ho WH, Tsai JT, Chou JH, Yue JB. Intelligent hybrid Taguchi-genetic algorithm for multi-criteria optimization of shaft alignment in marine vessels. IEEE Access. 2016;4:2304\u201313.","journal-title":"IEEE Access"},{"key":"4000_CR25","doi-asserted-by":"publisher","first-page":"1033","DOI":"10.3233\/JIFS-169878","volume":"36","author":"YJ Chen","year":"2019","unstructured":"Chen YJ, Ho WH. Evolutionary algorithm in adaptive neuro-fuzzy inference system for modeling the growth of foodborne fungi. J Intell Fuzzy Syst. 2019;36:1033\u20139.","journal-title":"J Intell Fuzzy Syst"},{"key":"4000_CR26","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.compeleceng.2018.03.019","volume":"67","author":"WH Tang","year":"2018","unstructured":"Tang WH, Chang YJ, Chen YJ, Ho WH. Genetic algorithm with Gaussian function for optimal P-wave morphology in electrocardiography for atrial fibrillation patients. Comput Electr Eng. 2018;67:52\u20137.","journal-title":"Comput Electr Eng"},{"issue":"1","key":"4000_CR27","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1016\/j.cmpb.2013.07.024","volume":"113","author":"MP Tarvainen","year":"2014","unstructured":"Tarvainen MP, Niskanen JP, Lipponen JA, Ranta-Aho PO, Karjalainen PA. Kubios HRV\u2013heart rate variability analysis software. Comput Methods Programs Biomed. 2014;113(1):210\u201320.","journal-title":"Comput Methods Programs Biomed"},{"key":"4000_CR28","volume-title":"Finland: biosignal analysis and medical imaging group (BSAMIG)","author":"MP Tarvainen","year":"2012","unstructured":"Tarvainen MP, Niskanen JP, Kubios HRV. Finland: biosignal analysis and medical imaging group (BSAMIG). Kuopio: Department of Applied Physics, University of Eastern Finland; 2012."},{"issue":"1","key":"4000_CR29","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1186\/1475-925X-8-38","volume":"8","author":"J Park","year":"2009","unstructured":"Park J, Lee S, Jeon M. Atrial fibrillation detection by heart rate variability in Poincare plot. Biomed Eng Online. 2009;8(1):38.","journal-title":"Biomed Eng Online"},{"issue":"3\u20134","key":"4000_CR30","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/S0378-4371(01)00144-3","volume":"295","author":"JW Kantelhardt","year":"2001","unstructured":"Kantelhardt JW, Koscielny-Bunde E, Rego HH, Havlin S, Bunde A. Detecting long-range correlations with detrended fluctuation analysis. Phys A. 2001;295(3\u20134):441\u201354.","journal-title":"Phys A"},{"key":"4000_CR31","volume-title":"Data mining: concepts and techniques","author":"J Han","year":"2011","unstructured":"Han J, Pei J, Kamber M. Data mining: concepts and techniques. Amsterdam: Elsevier; 2011."},{"key":"4000_CR32","doi-asserted-by":"crossref","DOI":"10.1002\/9781118874059","volume-title":"Discovering knowledge in data: an introduction to data mining","author":"DT Larose","year":"2014","unstructured":"Larose DT, Larose CD. Discovering knowledge in data: an introduction to data mining. Hoboken: Wiley; 2014."},{"issue":"1","key":"4000_CR33","doi-asserted-by":"publisher","first-page":"10","DOI":"10.46604\/aiti.2020.4284","volume":"5","author":"JS Sheu","year":"2020","unstructured":"Sheu JS, Han CY. Combining cloud computing and artificial intelligence scene recognition in real-time environment image planning walkable area. Adv Technol Innov. 2020;5(1):10\u20137.","journal-title":"Adv Technol Innov"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-021-04000-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-021-04000-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-021-04000-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,12]],"date-time":"2023-11-12T11:01:20Z","timestamp":1699786880000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-021-04000-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11]]},"references-count":33,"journal-issue":{"issue":"S5","published-print":{"date-parts":[[2021,11]]}},"alternative-id":["4000"],"URL":"https:\/\/doi.org\/10.1186\/s12859-021-04000-2","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11]]},"assertion":[{"value":"24 January 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 February 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 November 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Not applicable.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"93"}}