{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T04:31:17Z","timestamp":1771475477099,"version":"3.50.1"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T00:00:00Z","timestamp":1745539200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T00:00:00Z","timestamp":1745539200000},"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\/s42979-025-03954-x","type":"journal-article","created":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T06:43:33Z","timestamp":1745563413000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Prediction of Cardiovascular Disease using XGBoost with OPTUNA"],"prefix":"10.1007","volume":"6","author":[{"given":"Anamika","family":"Jain","sequence":"first","affiliation":[]},{"given":"Akansha","family":"Singh","sequence":"additional","affiliation":[]},{"given":"Aman","family":"Doherey","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,25]]},"reference":[{"key":"3954_CR1","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1016\/j.gheart.2018.09.511","volume":"13","author":"H Thomas","year":"2018","unstructured":"Thomas H, Diamond J, Vieco A, Chaudhuri S, Shinnar E, Cromer S, Perel P, Mensah GA, Narula J, Johnson CO, Roth GA. Global atlas of cardiovascular disease. Glob Heart. 2018;13:143\u201363.","journal-title":"Glob Heart"},{"key":"3954_CR2","doi-asserted-by":"publisher","first-page":"14659","DOI":"10.1109\/ACCESS.2019.2962755","volume":"8","author":"A Gupta","year":"2019","unstructured":"Gupta A, Kumar R, Arora HS, Raman B. MIFH: a machine intelligence framework for heart disease diagnosis. IEEE Access. 2019;8:14659\u201374.","journal-title":"IEEE Access"},{"key":"3954_CR3","doi-asserted-by":"crossref","unstructured":"Li J, Tu P, Wang H, Cao T, Zhang F. IEEE Access. IEEE, 8. 2020.","DOI":"10.1109\/ACCESS.2020.2969451"},{"key":"3954_CR4","doi-asserted-by":"publisher","unstructured":"Afaq S, Jain A. MAMMO-Net: an approach for classification of breast cancer using CNN with Gabor filter in mammographic images. In: International conference on computational intelligence and sustainable engineering solutions (CISES). 2022; p. 177\u201382. https:\/\/doi.org\/10.1109\/CISES54857.2022.9844320.","DOI":"10.1109\/CISES54857.2022.9844320"},{"key":"3954_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107245","volume":"103","author":"N Tran","year":"2020","unstructured":"Tran N, Schneider JG, Weber I, Qin AK. Hyper-parameter optimization in classification: to-do or not-to-do. Pattern Recognit. 2020;103: 107245.","journal-title":"Pattern Recognit"},{"key":"3954_CR6","doi-asserted-by":"crossref","unstructured":"Elumalai A, Maruthi P, Gautam N, Priyadharshini S, Suganthy M. Optimal prediction of attacks and arterial stiffness effects on heart disease by hybrid machine learning algorithm. J Amb Intell Hum Comput. 2021;1\u201311.","DOI":"10.1007\/s12652-020-02706-4"},{"key":"3954_CR7","doi-asserted-by":"publisher","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. An intelligent learning system based on random search algorithm and optimized random forest model for improved heart disease detection. IEEE Access. 2019;7:180235\u201343.","journal-title":"IEEE Access"},{"issue":"4","key":"3954_CR8","doi-asserted-by":"publisher","first-page":"3409","DOI":"10.1007\/s13369-020-05105-1","volume":"46","author":"A Mehmood","year":"2021","unstructured":"Mehmood A, et al. Prediction of heart disease using deep convolutional neural networks. Arab J Sci Eng. 2021;46(4):3409\u201322.","journal-title":"Arab J Sci Eng"},{"key":"3954_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2021.107651","volume":"161","author":"AA Samir","year":"2021","unstructured":"Samir AA, Rashwan AR, Sallam KM, Chakrabortty RK, Ryan MJ, Abohany AA. Evolutionary algorithm-based convolutional neural network for predicting heart diseases. Comput Ind Eng. 2021;161: 107651.","journal-title":"Comput Ind Eng"},{"key":"3954_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2019.100203","volume":"16","author":"CBC Latha","year":"2019","unstructured":"Latha CBC, Jeeva SC. Improving the accuracy of prediction of heart disease risk based on ensemble classification techniques. Inf Med Unlocked. 2019;16: 100203.","journal-title":"Inf Med Unlocked"},{"key":"3954_CR11","doi-asserted-by":"publisher","first-page":"59247","DOI":"10.1109\/ACCESS.2020.2981159","volume":"8","author":"C Guo","year":"2020","unstructured":"Guo C, Zhang J, Liu Y, Xie Y, Han Z, Yu J. Recursion enhanced random forest with an improved linear model (RERF-ILM) for heart disease detection on the internet of medical things platform. IEEE Access. 2020;8:59247\u201356.","journal-title":"IEEE Access"},{"key":"3954_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.102474","volume":"66","author":"P Li","year":"2021","unstructured":"Li P, Hu Y, Liu ZP. Prediction of cardiovascular diseases by integrating multi-modal features with machine learning methods. Biomed Signal Process Control. 2021;66: 102474.","journal-title":"Biomed Signal Process Control"},{"issue":"18","key":"3954_CR13","doi-asserted-by":"publisher","first-page":"11703","DOI":"10.1007\/s00521-021-05820-2","volume":"33","author":"MS Memon","year":"2021","unstructured":"Memon MS, Lakhan A, Mohammed MA, Qabulio M, Al-Turjman F, Abdulkareem KH. Machine learning-data mining integrated approach for premature ventricular contraction prediction. Neural Comput Appl. 2021;33(18):11703\u201319.","journal-title":"Neural Comput Appl"},{"key":"3954_CR14","doi-asserted-by":"publisher","first-page":"9846","DOI":"10.1109\/ACCESS.2020.2964294","volume":"8","author":"X Gu","year":"2020","unstructured":"Gu X, Han Y, Yu J. A novel lane-changing decision model for autonomous vehicles based on deep autoencoder network and XGBoost. IEEE Access. 2020;8:9846\u201363.","journal-title":"IEEE Access"},{"key":"3954_CR15","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. An automated diagnostic system for heart disease prediction based on $${\\chi ^{2}}$$ statistical model and optimally configured deep neural network. IEEE Access. 2019;7:34938\u201345.","journal-title":"IEEE Access"},{"key":"3954_CR16","doi-asserted-by":"crossref","unstructured":"Nugroho A, Suhartanto H, September. Hyper-parameter tuning based on random search for DenseNet optimization. In: 2020 7th international conference on information technology, computer, and electrical engineering (ICITACEE); 2020. p. 96\u20139. IEEE.","DOI":"10.1109\/ICITACEE50144.2020.9239164"},{"key":"3954_CR17","doi-asserted-by":"publisher","unstructured":"Nugroho A, Suhartanto H. Hyper-parameter tuning based on random search for DenseNet optimization. In: Proceedings of the 7th international conference on information technology, computer, and electrical engineering (ICITACEE 2020);2020. p. 96\u20139. https:\/\/doi.org\/10.1109\/ICITACEE50144.2020.9239164.","DOI":"10.1109\/ICITACEE50144.2020.9239164"},{"issue":"1","key":"3954_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12911-020-1023-5","volume":"20","author":"D Chicco","year":"2020","unstructured":"Chicco D, Jurman G. Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone. BMC Med Inform Decis Mak. 2020;20(1):1\u201316.","journal-title":"BMC Med Inform Decis Mak"},{"key":"3954_CR19","unstructured":"Cleveland Database. http:\/\/archive.ics.uci.edu\/ml\/datasets\/Heart+Disease."},{"key":"3954_CR20","doi-asserted-by":"publisher","first-page":"81542","DOI":"10.1109\/ACCESS.2019.2923707","volume":"7","author":"S Mohan","year":"2019","unstructured":"Mohan S, Thirumalai C, Srivastava G. Effective heart disease prediction using hybrid machine learning techniques. IEEE Access. 2019;7:81542\u201354.","journal-title":"IEEE Access"},{"key":"3954_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2020.106628","volume":"84","author":"SMS Shah","year":"2020","unstructured":"Shah SMS, Shah FA, Hussain SA, Batool S. Support vector machines-based heart disease diagnosis using feature subset, wrapping selection and extraction methods. Comput Electr Eng. 2020;84: 106628.","journal-title":"Comput Electr Eng"},{"issue":"2","key":"3954_CR22","first-page":"52","volume":"32","author":"E Siegel","year":"1991","unstructured":"Siegel E. German standards for ventilation devices. Anasthesiol Intensivmed. 1991;32(2):52\u20134.","journal-title":"Anasthesiol Intensivmed"},{"issue":"4","key":"3954_CR23","doi-asserted-by":"publisher","first-page":"7675","DOI":"10.1016\/j.eswa.2008.09.013","volume":"36","author":"R Das","year":"2009","unstructured":"Das R, Turkoglu I, Sengur A. Effective diagnosis of heart disease through neural networks ensembles. Expert Syst Appl. 2009;36(4):7675\u201380.","journal-title":"Expert Syst Appl"},{"key":"3954_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.103456","volume":"73","author":"P Srinivas","year":"2022","unstructured":"Srinivas P, Katarya R. hyOPTXg: OPTUNA hyper-parameter optimization framework for predicting cardiovascular disease using XGBoost. Biomed Signal Process Control. 2022;73: 103456.","journal-title":"Biomed Signal Process Control"},{"issue":"1","key":"3954_CR25","doi-asserted-by":"publisher","first-page":"514","DOI":"10.1038\/s41598-024-51184-7","volume":"14","author":"AU Rahman","year":"2024","unstructured":"Rahman AU, et al. Enhancing heart disease prediction using a self-attention-based transformer model. Sci Rep. 2024;14(1):514.","journal-title":"Sci Rep"},{"key":"3954_CR26","doi-asserted-by":"crossref","unstructured":"Aljaaf AJ, Al-Jumeily D, Hussain AJ, Dawson T, Fergus P, Al-Jumaily M. Predicting the likelihood of heart failure with a multi level risk assessment using decision tree. In: 2015 third international conference on technological advances in electrical, electronics and computer engineering (TAEECE);2015. p. 101\u20136. IEEE.","DOI":"10.1109\/TAEECE.2015.7113608"},{"key":"3954_CR27","doi-asserted-by":"crossref","unstructured":"Gabriel JJ, Anbarasi LJ. Accurate cardiovascular disease prediction: leveraging $$Opt_hpLGBM$$ with dual-tier feature selection. IEEE Access. 2024.","DOI":"10.1109\/ACCESS.2024.3470537"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-025-03954-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-025-03954-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-025-03954-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T06:43:43Z","timestamp":1745563423000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-025-03954-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,25]]},"references-count":27,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["3954"],"URL":"https:\/\/doi.org\/10.1007\/s42979-025-03954-x","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,25]]},"assertion":[{"value":"14 December 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 April 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 April 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":"We have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human and\/or animals rights"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}],"article-number":"421"}}