{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:46:04Z","timestamp":1775069164631,"version":"3.50.1"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,2,17]],"date-time":"2025-02-17T00:00:00Z","timestamp":1739750400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,2,17]],"date-time":"2025-02-17T00:00:00Z","timestamp":1739750400000},"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":["SN COMPUT. SCI."],"DOI":"10.1007\/s42979-025-03723-w","type":"journal-article","created":{"date-parts":[[2025,2,17]],"date-time":"2025-02-17T21:21:11Z","timestamp":1739827271000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Cardiovascular Disease Prediction Using Particle Swarm Optimization and Neural Network Based an Integrated Framework"],"prefix":"10.1007","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-5709-4637","authenticated-orcid":false,"given":"S. Ramchandra","family":"Reddy","sequence":"first","affiliation":[]},{"given":"G. Vishnu","family":"Murthy","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,2,17]]},"reference":[{"issue":"1","key":"3723_CR1","doi-asserted-by":"publisher","first-page":"715","DOI":"10.1038\/s41467-021-20966-2","volume":"12","author":"R Zeleznik","year":"2021","unstructured":"Zeleznik R, Foldyna B, Eslami P, Weiss J, Alexander I, Taron J, Parmar C, Alvi RM, Banerji D, Uno M, et al. Deep convolutional neural networks to predict cardiovascular risk from computed tomography. Nat Commun. 2021;12(1):715.","journal-title":"Nat Commun"},{"key":"3723_CR2","unstructured":"World\u00a0Health Organization. Global status report on alcohol and health 2018. World Health Organization. 2019."},{"key":"3723_CR3","doi-asserted-by":"crossref","unstructured":"Alfadli KM, Almagrabi AO. Feature-limited prediction on the uci heart disease dataset. Comput Mater Contin. 2023;74(3).","DOI":"10.32604\/cmc.2023.033603"},{"key":"3723_CR4","unstructured":"https:\/\/sci2s.ugr.es\/keel\/dataset.php?cod=57."},{"key":"3723_CR5","unstructured":"https:\/\/www.kaggle.com\/datasets\/aasheesh200\/framingham-heart-study-dataset."},{"key":"3723_CR6","unstructured":"https:\/\/www.kaggle.com\/datasets\/sulianova\/cardiovascular-disease-dataset."},{"key":"3723_CR7","doi-asserted-by":"crossref","unstructured":"Rajliwall NS, Davey R, Chetty G. Machine learning based models for cardiovascular risk prediction. In: 2018 international conference on machine learning and data engineering (ICMLDE). 2018;142\u2013148. IEEE.","DOI":"10.1109\/iCMLDE.2018.00034"},{"key":"3723_CR8","unstructured":"Rubini PE, Subasini C, Katharine A V, Kumaresan V, Kumar SG, Nithya T. A cardiovascular disease prediction using machine learning algorithms. Ann Roman Soc Cell Biol. 2021:904\u2013912."},{"key":"3723_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijcha.2020.100521","volume":"28","author":"FM Wekesah","year":"2020","unstructured":"Wekesah FM, Mutua MK, Boateng D, Grobbee DE, Asiki G, Kyobutungi CK, Grobusch KK. Comparative performance of pooled cohort equations and framingham risk scores in cardiovascular disease risk classification in a slum setting in nairobi kenya. IJC Heart Vasculat. 2020;28: 100521.","journal-title":"IJC Heart Vasculat"},{"issue":"4","key":"3723_CR10","doi-asserted-by":"publisher","DOI":"10.1161\/JAHA.119.013924","volume":"9","author":"RK Sevakula","year":"2020","unstructured":"Sevakula RK, Au-Yeung WTM, Singh JP, Heist EK, Isselbacher EM, Armoundas AA. State-of-the-art machine learning techniques aiming to improve patient outcomes pertaining to the cardiovascular system. J Am Heart Assoc. 2020;9(4): e013924.","journal-title":"J Am Heart Assoc"},{"key":"3723_CR11","doi-asserted-by":"crossref","unstructured":"Takahashi D, Fujimoto S, Nozaki Y O, Kudo A, Kawaguchi Y O, Takamur K, Hiki M, Sato E, Tomizawa N, Daida H, et al. Fully automated coronary artery calcium quantification on electrocardiogram-gated non-contrast cardiac computed tomography using deep-learning with novel Heart-labelling method. Eur Heart J Open. 2023;3(6):ocad113.","DOI":"10.1093\/ehjopen\/oead113"},{"issue":"3","key":"3723_CR12","doi-asserted-by":"publisher","first-page":"426","DOI":"10.3390\/life12030426","volume":"12","author":"PR Kshirsagar","year":"2022","unstructured":"Kshirsagar PR, Manoharan H, Shitharth S, Alshareef AM, Albishry N, Balachandran PK. Deep learning approaches for prognosis of automated skin disease. Life. 2022;12(3):426.","journal-title":"Life"},{"issue":"1","key":"3723_CR13","doi-asserted-by":"publisher","first-page":"5590","DOI":"10.1038\/s41598-024-55098-2","volume":"14","author":"KK Jyothi","year":"2024","unstructured":"Jyothi KK, Borra SR, Srilakshmi K, Balachandran PK, Reddy GP, Colak I, Dhanamjayulu C, Chinthaginjala R, Khan B. A novel optimized neural network model for cyber attack detection using enhanced whale optimization algorithm. Sci Rep. 2024;14(1):5590.","journal-title":"Sci Rep"},{"issue":"1","key":"3723_CR14","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.swevo.2011.02.002","volume":"1","author":"J Derrac","year":"2011","unstructured":"Derrac J, Garc\u00eda S, Molina D, Herrera F. A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evolut Comput. 2011;1(1):3\u201318.","journal-title":"Swarm Evolut Comput"},{"key":"3723_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2021.100584","volume":"24","author":"MN Uddin","year":"2021","unstructured":"Uddin MN, Halder RK. An ensemble method based multilayer dynamic system to predict cardiovascular disease using machine learning approach. Inform Med Unlock. 2021;24: 100584.","journal-title":"Inform Med Unlock"},{"issue":"2","key":"3723_CR16","first-page":"203","volume":"40","author":"X Zeng","year":"2021","unstructured":"Zeng X, Li H, et al. Application of machine learning in disease prediction. J Biomed Eng Res. 2021;40(2):203\u20139.","journal-title":"J Biomed Eng Res"},{"issue":"5","key":"3723_CR17","doi-asserted-by":"publisher","first-page":"366","DOI":"10.3390\/jpm11050366","volume":"11","author":"SJ Lee","year":"2021","unstructured":"Lee SJ, Cartmell KB. An association rule mining analysis of lifestyle behavioral risk factors in cancer survivors with high cardiovascular disease risk. J Person Med. 2021;11(5):366.","journal-title":"J Person Med"},{"key":"3723_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104672","volume":"136","author":"MM Ali","year":"2021","unstructured":"Ali MM, Paul BK, Ahmed K, Bui FM, Quinn JM, Moni MA. Heart disease prediction using supervised machine learning algorithms: performance analysis and comparison. Comput Biol Med. 2021;136: 104672.","journal-title":"Comput Biol Med"},{"issue":"07","key":"3723_CR19","first-page":"1","volume":"20","author":"TA Srinivas","year":"2023","unstructured":"Srinivas TA, David D. Decoding destiny: harnessing machine learning for breast cancer survival prediction. Indian J Eng. 2023;20(07):1\u20138.","journal-title":"Indian J Eng"},{"key":"3723_CR20","unstructured":"https:\/\/www.kaggle.com\/datasets\/sulianova\/cardiovascular-disease-dataset."},{"key":"3723_CR21","doi-asserted-by":"crossref","unstructured":"Muhammad G, Naveed S, Nadeem L, Mahmood T, Khan A R, Amin Y, Bahaj S A O. Enhancing prognosis accuracy for ischemic cardiovascular disease using k nearest neighbor algorithm: a robust approach. IEEE Access. 2023.","DOI":"10.1109\/ACCESS.2023.3312046"},{"issue":"2","key":"3723_CR22","doi-asserted-by":"publisher","first-page":"56","DOI":"10.60084\/ijds.v1i2.131","volume":"1","author":"R Suhendra","year":"2023","unstructured":"Suhendra R, Husdayanti N, Suryadi S, Juliwardi I, Sanusi S, Ridho A, Ardiansyah M, Murhaban M, Ikhsan I. Cardiovascular disease prediction using gradient boosting classifier. Infolitika J Data Sci. 2023;1(2):56\u201362.","journal-title":"Infolitika J Data Sci"},{"issue":"1","key":"3723_CR23","doi-asserted-by":"publisher","first-page":"5245","DOI":"10.1038\/s41598-020-62133-5","volume":"10","author":"L Yang","year":"2020","unstructured":"Yang L, Wu H, Jin X, Zheng P, Hu S, Xu X, Yu W, Yan J. Study of cardiovascular disease prediction model based on random forest in eastern china. Sci Rep. 2020;10(1):5245.","journal-title":"Sci Rep"},{"issue":"2","key":"3723_CR24","first-page":"7456","volume":"5","author":"J Jinitha","year":"2023","unstructured":"Jinitha J, et al. Empowering coronary artery disease prediction through feature optimization with ensemble learning based hybrid bagging and boosting techniques. J Res Admin. 2023;5(2):7456\u201383.","journal-title":"J Res Admin"},{"key":"3723_CR25","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1007\/s10617-018-9205-4","volume":"22","author":"K Mathan","year":"2018","unstructured":"Mathan K, Kumar PM, Panchatcharam P, Manogaran G, Varadharajan R. A novel gini index decision tree data mining method with neural network classifiers for prediction of heart disease. Design Autom Embed Syst. 2018;22:225\u201342.","journal-title":"Design Autom Embed Syst"},{"issue":"1","key":"3723_CR26","first-page":"345","volume":"11","author":"KS Nugroho","year":"2022","unstructured":"Nugroho KS, Sukmadewa AY, Vidianto A, Mahmudy WF. Effective predictive modelling for coronary artery diseases using support vector machine. IAES Int J Artif Intell. 2022;11(1):345.","journal-title":"IAES Int J Artif Intell"},{"issue":"12","key":"3723_CR27","doi-asserted-by":"publisher","first-page":"1409","DOI":"10.1080\/10255842.2022.2078966","volume":"25","author":"D Deepika","year":"2022","unstructured":"Deepika D, Balaji N. Effective heart disease prediction with grey-wolf with firefly algorithm-differential evolution (gf-de) for feature selection and weighted ann classification. Comput Methods Biomech Biomed Eng. 2022;25(12):1409\u201327.","journal-title":"Comput Methods Biomech Biomed Eng"},{"key":"3723_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.108411","volume":"242","author":"P Sharma","year":"2022","unstructured":"Sharma P, Dinkar SK. A linearly adaptive sine-cosine algorithm with application in deep neural network for feature optimization in arrhythmia classification using ecg signals. Knowl Based Syst. 2022;242: 108411.","journal-title":"Knowl Based Syst"},{"key":"3723_CR29","doi-asserted-by":"crossref","unstructured":"Sharma H, Agarwal R. Updated frequency-based bat algorithm (ufbba) for feature selection and vote classifier in predicting heart disease. In: Advances in Computer, Communication and Computational Sciences: Proceedings of IC4S 2019, pages 449\u2013460. Springer. 2021.","DOI":"10.1007\/978-981-15-4409-5_41"},{"issue":"8","key":"3723_CR30","doi-asserted-by":"publisher","first-page":"4503","DOI":"10.1007\/s11760-023-02684-y","volume":"17","author":"R Divya","year":"2023","unstructured":"Divya R, Shadrach FD, Padmaja S. Cardiovascular risk detection using harris hawks optimization with ensemble learning model on ppg signals. Signal Image Video Process. 2023;17(8):4503\u201312.","journal-title":"Signal Image Video Process"},{"issue":"22","key":"3723_CR31","doi-asserted-by":"publisher","first-page":"4621","DOI":"10.3390\/math11224621","volume":"11","author":"F Alqurashi","year":"2023","unstructured":"Alqurashi F, Zafar A, Khan AI, Almalawi A, Alam MM, Azim R. Deep neural network and predator crow optimization-based intelligent healthcare system for predicting cardiac diseases. Mathematics. 2023;11(22):4621.","journal-title":"Mathematics"},{"key":"3723_CR32","doi-asserted-by":"crossref","unstructured":"Subanya B, Rajalaxmi R. Feature selection using artificial bee colony for cardiovascular disease classification. In: 2014 International conference on electronics and communication systems (ICECS), 1\u20136. IEEE. 2014.","DOI":"10.1109\/ECS.2014.6892729"},{"key":"3723_CR33","doi-asserted-by":"crossref","unstructured":"Yazid M H B A, Talib M S, Satria M H. Flower pollination neural network for heart disease classification. In: IOP Conference Series: Materials Science and Engineering. 2019;551:012072.","DOI":"10.1088\/1757-899X\/551\/1\/012072"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-025-03723-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-025-03723-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-025-03723-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,17]],"date-time":"2025-02-17T21:21:19Z","timestamp":1739827279000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-025-03723-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,17]]},"references-count":33,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["3723"],"URL":"https:\/\/doi.org\/10.1007\/s42979-025-03723-w","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,17]]},"assertion":[{"value":"29 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 February 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not Applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not Applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed Consent"}}],"article-number":"186"}}