{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T02:47:55Z","timestamp":1783133275820,"version":"3.54.6"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T00:00:00Z","timestamp":1702512000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T00:00:00Z","timestamp":1702512000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Cardiovascular diseases (CVDs) are a serious public health issue that affects and is responsible for numerous fatalities and impairments. Ischemic heart disease (IHD) is one of the most prevalent and deadliest types of CVDs and is responsible for 45% of all CVD-related fatalities. IHD occurs when the blood supply to the heart is reduced due to narrowed or blocked arteries, which causes angina pectoris (AP) chest pain. AP is a common symptom of IHD and can indicate a higher risk of heart attack or sudden cardiac death. Therefore, it is important to diagnose and treat AP promptly and effectively. To forecast AP in women, we constructed a novel artificial intelligence (AI) method employing the tree-based algorithm known as an Explainable Boosting Machine (EBM). EBM is a machine learning (ML) technique that combines the interpretability of linear models with the flexibility and accuracy of gradient boosting. We applied EBM to a dataset of 200 female patients, 100 with AP and 100 without AP, and extracted the most relevant features for AP prediction. We then evaluated the performance of EBM against other AI methods, such as Logistic Regression (LR), Categorical Boosting (CatBoost), eXtreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Light Gradient Boosting Machine (LightGBM). We found that EBM was the most accurate and well-balanced technique for forecasting AP, with accuracy (0.925) and Youden's index (0.960). We also looked at the global and local explanations provided by EBM to better understand how each feature affected the prediction and how each patient was classified. Our research showed that EBM is a useful AI method for predicting AP in women and identifying the risk factors related to it. This can help clinicians to provide personalized and evidence-based care for female patients with AP.<\/jats:p>","DOI":"10.1038\/s41598-023-49673-2","type":"journal-article","created":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T04:22:54Z","timestamp":1702527774000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["A proposed tree-based explainable artificial intelligence approach for the prediction of angina pectoris"],"prefix":"10.1038","volume":"13","author":[{"given":"Emek","family":"Guldogan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fatma Hilal","family":"Yagin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdulvahap","family":"Pinar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cemil","family":"Colak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seifedine","family":"Kadry","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jungeun","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,12,14]]},"reference":[{"key":"49673_CR1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12889-020-10013-y","volume":"21","author":"S Rehman","year":"2021","unstructured":"Rehman, S., Rehman, E., Ikram, M. & Jianglin, Z. Cardiovascular disease (CVD): Assessment, prediction and policy implications. BMC Public Health 21, 1\u201314 (2021).","journal-title":"BMC Public Health"},{"key":"49673_CR2","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1155\/2023\/2404806","volume":"2023","author":"F Masoudkabir","year":"2023","unstructured":"Masoudkabir, F. et al. Shared lifestyle-related risk factors of cardiovascular disease and cancer: Evidence for joint prevention. Sci. World J. 2023, 240 (2023).","journal-title":"Sci. World J."},{"key":"49673_CR3","doi-asserted-by":"publisher","first-page":"658","DOI":"10.3390\/jpm11070658","volume":"11","author":"G Battineni","year":"2021","unstructured":"Battineni, G., Sagaro, G. G., Chintalapudi, N. & Amenta, F. The benefits of telemedicine in personalized prevention of cardiovascular diseases (CVD): A systematic review. J. Pers. Med. 11, 658 (2021).","journal-title":"J. Pers. Med."},{"key":"49673_CR4","first-page":"9349","volume":"12","author":"MA Khan","year":"2020","unstructured":"Khan, M. A. et al. Global epidemiology of ischemic heart disease: Results from the global burden of disease study. Cureus 12, 9349 (2020).","journal-title":"Cureus"},{"key":"49673_CR5","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1080\/14779072.2021.1855975","volume":"19","author":"N Peer","year":"2021","unstructured":"Peer, N., Baatiema, L. & Kengne, A.-P. Ischaemic heart disease, stroke, and their cardiometabolic risk factors in Africa: Current challenges and outlook for the future. Expert Rev. Cardiovasc. Ther. 19, 129\u2013140 (2021).","journal-title":"Expert Rev. Cardiovasc. Ther."},{"key":"49673_CR6","doi-asserted-by":"publisher","first-page":"4189","DOI":"10.1007\/s12325-022-02222-1","volume":"39","author":"A Nedoshivin","year":"2022","unstructured":"Nedoshivin, A., Petrova, P. T. & Karpov, Y. Efficacy and safety of Ivabradine in combination with beta-blockers in patients with stable angina pectoris: A systematic review and meta-analysis. Adv. Ther. 39, 4189\u20134204 (2022).","journal-title":"Adv. Ther."},{"key":"49673_CR7","doi-asserted-by":"publisher","first-page":"6891","DOI":"10.3390\/jcm11236891","volume":"11","author":"R Manfredi","year":"2022","unstructured":"Manfredi, R. et al. Angina in 2022: Current perspectives. J. Clin. Med. 11, 6891 (2022).","journal-title":"J. Clin. Med."},{"key":"49673_CR8","doi-asserted-by":"publisher","first-page":"923","DOI":"10.1016\/S0735-1097(02)01716-3","volume":"39","author":"MC Kim","year":"2002","unstructured":"Kim, M. C., Kini, A. & Sharma, S. K. Refractory angina pectoris: Mechanism and therapeutic options. J. Am. Coll. Cardiol. 39, 923\u2013934 (2002).","journal-title":"J. Am. Coll. Cardiol."},{"key":"49673_CR9","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1016\/j.ijcard.2012.07.001","volume":"163","author":"C Zuchi","year":"2013","unstructured":"Zuchi, C., Tritto, I. & Ambrosio, G. Angina pectoris in women: Focus on microvascular disease. Int. J. Cardiol. 163, 132\u2013140 (2013).","journal-title":"Int. J. Cardiol."},{"key":"49673_CR10","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1136\/jech.41.3.215","volume":"41","author":"D Mant","year":"1987","unstructured":"Mant, D., Villard-Mackintosh, L., Vessey, M. & Yeates, D. Myocardial infarction and angina pectoris in young women. J. Epidemiol. Commun. Health 41, 215\u2013219 (1987).","journal-title":"J. Epidemiol. Commun. Health"},{"key":"49673_CR11","doi-asserted-by":"publisher","first-page":"71","DOI":"10.2174\/157340310790231608","volume":"6","author":"K Banks","year":"2010","unstructured":"Banks, K., Lo, M. & Khera, A. Angina in women without obstructive coronary artery disease. Curr. Cardiol. Rev. 6, 71\u201381 (2010).","journal-title":"Curr. Cardiol. Rev."},{"key":"49673_CR12","doi-asserted-by":"publisher","first-page":"455","DOI":"10.3390\/medicina56090455","volume":"56","author":"HSR Rajula","year":"2020","unstructured":"Rajula, H. S. R., Verlato, G., Manchia, M., Antonucci, N. & Fanos, V. Comparison of conventional statistical methods with machine learning in medicine: Diagnosis, drug development, and treatment. Medicina 56, 455 (2020).","journal-title":"Medicina"},{"key":"49673_CR13","doi-asserted-by":"publisher","first-page":"5135","DOI":"10.3390\/app10155135","volume":"10","author":"N Caball\u00e9-Cervig\u00f3n","year":"2020","unstructured":"Caball\u00e9-Cervig\u00f3n, N., Castillo-Sequera, J. L., G\u00f3mez-Pulido, J. A., G\u00f3mez-Pulido, J. M. & Polo-Luque, M. L. Machine learning applied to diagnosis of human diseases: A systematic review. Appl. Sci. 10, 5135 (2020).","journal-title":"Appl. Sci."},{"key":"49673_CR14","volume":"3","author":"A Ray","year":"2021","unstructured":"Ray, A. & Chaudhuri, A. K. Smart healthcare disease diagnosis and patient management: Innovation, improvement and skill development. Mach. Learn. Appl. 3, 100011 (2021).","journal-title":"Mach. Learn. Appl."},{"key":"49673_CR15","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1007\/s44196-023-00182-0","volume":"16","author":"X Zhang","year":"2023","unstructured":"Zhang, X. & Xu, H. Prediction of angina pectoris events in middle-aged and elderly people using RR interval time series in the resting state: A cohort study based on SHHS. Int. J. Comput. Intell. Syst. 16, 10 (2023).","journal-title":"Int. J. Comput. Intell. Syst."},{"key":"49673_CR16","doi-asserted-by":"publisher","DOI":"10.3389\/fphar.2021.659099","volume":"12","author":"Z Yu","year":"2021","unstructured":"Yu, Z. et al. Predicting adverse drug events in Chinese pediatric inpatients with the associated risk factors: A machine learning study. Front. Pharmacol. 12, 659099 (2021).","journal-title":"Front. Pharmacol."},{"key":"49673_CR17","doi-asserted-by":"publisher","first-page":"1682","DOI":"10.3390\/jpm12101682","volume":"12","author":"A Hasuike","year":"2022","unstructured":"Hasuike, A. et al. Machine learning in predicting tooth loss: A systematic review and risk of bias assessment. J. Pers. Med. 12, 1682 (2022).","journal-title":"J. Pers. Med."},{"key":"49673_CR18","doi-asserted-by":"crossref","unstructured":"Sarica, A., Quattrone, A. & Quattrone, A. International Conference on Brain Informatics. 341\u2013350 (Springer, 2021).","DOI":"10.1007\/978-3-030-86993-9_31"},{"key":"49673_CR19","unstructured":"Kagglersn. Predict Angina (Prediction of Angina from Healthcare). https:\/\/www.kaggle.com\/datasets\/snehal1409\/predict-angina (2023)."},{"key":"49673_CR20","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1016\/S0895-4356(00)00314-0","volume":"54","author":"R Bender","year":"2001","unstructured":"Bender, R. & Lange, S. Adjusting for multiple testing\u2014When and how?. J. Clin. Epidemiol. 54, 343\u2013349 (2001).","journal-title":"J. Clin. Epidemiol."},{"key":"49673_CR21","first-page":"282","volume":"71","author":"AK Arslan","year":"2021","unstructured":"Arslan, A. K. et al. Prediction of postcoronary artery bypass grafting atrial fibrillation: POAFRiskScore tool. Thorac. Cardiovasc. Surg. 71, 282\u2013290 (2021).","journal-title":"Thorac. Cardiovasc. Surg."},{"key":"49673_CR22","doi-asserted-by":"publisher","first-page":"224","DOI":"10.5336\/biostatic.2018-62787","volume":"10","author":"A Arslan","year":"2018","unstructured":"Arslan, A., Ya\u015far, \u015e, Colak, C. & Yolo\u011flu, S. WSSPAS: An interactive web application for sample size and power analysis with R using shiny. Turk. Klin. J. Biostat. 10, 224\u2013246 (2018).","journal-title":"Turk. Klin. J. Biostat."},{"key":"49673_CR23","doi-asserted-by":"publisher","first-page":"3735","DOI":"10.1016\/j.csda.2009.04.009","volume":"53","author":"J-H Kim","year":"2009","unstructured":"Kim, J.-H. Estimating classification error rate: Repeated cross-validation, repeated hold-out and bootstrap. Comput. Stat. Data Anal. 53, 3735\u20133745 (2009).","journal-title":"Comput. Stat. Data Anal."},{"key":"49673_CR24","doi-asserted-by":"publisher","first-page":"4920","DOI":"10.3390\/app12104920","volume":"12","author":"G Macin","year":"2022","unstructured":"Macin, G. et al. An accurate multiple sclerosis detection model based on exemplar multiple parameters local phase quantization: ExMPLPQ. Appl. Sci. 12, 4920 (2022).","journal-title":"Appl. Sci."},{"key":"49673_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106619","volume":"154","author":"FH Yagin","year":"2023","unstructured":"Yagin, F. H. et al. Explainable artificial intelligence model for identifying COVID-19 gene biomarkers. Comput. Biol. Med. 154, 106619 (2023).","journal-title":"Comput. Biol. Med."},{"key":"49673_CR26","doi-asserted-by":"publisher","first-page":"3875","DOI":"10.3390\/app13063875","volume":"13","author":"FH Yagin","year":"2023","unstructured":"Yagin, F. H. et al. Estimation of obesity levels with a trained neural network approach optimized by the Bayesian technique. Appl. Sci. 13, 3875 (2023).","journal-title":"Appl. Sci."},{"key":"49673_CR27","unstructured":"Martino, A., De Santis, E., Baldini, L. & Rizzi, A. IJCCI. 487\u2013495."},{"key":"49673_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2021.103758","volume":"117","author":"Y Huang","year":"2021","unstructured":"Huang, Y., Jiang, X., Gabriel, R. A. & Ohno-Machado, L. Calibrating predictive model estimates in a distributed network of patient data. J. Biomed. Inform. 117, 103758 (2021).","journal-title":"J. Biomed. Inform."},{"key":"49673_CR29","first-page":"1464","volume":"34","author":"B-H Huang","year":"2015","unstructured":"Huang, B.-H., Zhang, H., Sun, Z.-J. & Zhou, L.-X. Forest fire danger factors and their division in Shandong based on GIS and RS. Chin. J. Ecol. 34, 1464 (2015).","journal-title":"Chin. J. Ecol."},{"key":"49673_CR30","unstructured":"Chen, J.-X., Cheng, T.-H., Chan, A. L. & Wang, H.-Y. 2004 IDEAS Workshop on Medical Information Systems: The Digital Hospital (IDEAS-DH'04). 35\u201339 (IEEE, 2004)."},{"key":"49673_CR31","unstructured":"Dorogush, A. V., Ershov, V. & Gulin, A. CatBoost: Gradient boosting with categorical features support. arXiv Preprint arXiv:1810.11363 (2018)."},{"key":"49673_CR32","first-page":"336","volume":"31","author":"L Prokhorenkova","year":"2018","unstructured":"Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V. & Gulin, A. CatBoost: Unbiased boosting with categorical features. Adv. Neural Inf. Process. Syst. 31, 336 (2018).","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"49673_CR33","doi-asserted-by":"crossref","unstructured":"Bakhareva, N. et al. 2019 International Russian Automation Conference (RusAutoCon). 1\u20136 (IEEE, 2019).","DOI":"10.1109\/RUSAUTOCON.2019.8867696"},{"key":"49673_CR34","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1006\/jcss.1997.1504","volume":"55","author":"Y Freund","year":"1997","unstructured":"Freund, Y. & Schapire, R. E. A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55, 119\u2013139 (1997).","journal-title":"J. Comput. Syst. Sci."},{"key":"49673_CR35","doi-asserted-by":"publisher","DOI":"10.1201\/b12207","volume-title":"Ensemble Methods: Foundations and Algorithms","author":"Z-H Zhou","year":"2012","unstructured":"Zhou, Z.-H. Ensemble Methods: Foundations and Algorithms (CRC Press, 2012)."},{"key":"49673_CR36","doi-asserted-by":"publisher","first-page":"471","DOI":"10.1016\/j.jhydrol.2017.03.020","volume":"548","author":"SA Naghibi","year":"2017","unstructured":"Naghibi, S. A., Moghaddam, D. D., Kalantar, B., Pradhan, B. & Kisi, O. A comparative assessment of GIS-based data mining models and a novel ensemble model in groundwater well potential mapping. J. Hydrol. 548, 471\u2013483 (2017).","journal-title":"J. Hydrol."},{"key":"49673_CR37","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1109\/TNN.2008.2005601","volume":"20","author":"PA Est\u00e9vez","year":"2009","unstructured":"Est\u00e9vez, P. A., Tesmer, M., Perez, C. A. & Zurada, J. M. Normalized mutual information feature selection. IEEE Trans. Neural Netw. 20, 189\u2013201 (2009).","journal-title":"IEEE Trans. Neural Netw."},{"key":"49673_CR38","unstructured":"Chen, T. & Guestrin, C. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 785\u2013794."},{"key":"49673_CR39","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1214\/aos\/1013203451","volume":"29","author":"JH Friedman","year":"2001","unstructured":"Friedman, J. H. Greedy function approximation: A gradient boosting machine. Ann. Stat. 29, 1189\u20131232 (2001).","journal-title":"Ann. Stat."},{"key":"49673_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrmms.2021.104856","volume":"145","author":"J Zhou","year":"2021","unstructured":"Zhou, J., Qiu, Y., Khandelwal, M., Zhu, S. & Zhang, X. Developing a hybrid model of Jaya algorithm-based extreme gradient boosting machine to estimate blast-induced ground vibrations. Int. J. Rock Mech. Min. Sci. 145, 104856 (2021).","journal-title":"Int. J. Rock Mech. Min. Sci."},{"key":"49673_CR41","first-page":"1","volume":"21","author":"SB Jabeur","year":"2021","unstructured":"Jabeur, S. B., Mefteh-Wali, S. & Viviani, J.-L. Forecasting gold price with the XGBoost algorithm and SHAP interaction values. Ann. Oper. Res. 21, 1\u201321 (2021).","journal-title":"Ann. Oper. Res."},{"key":"49673_CR42","first-page":"1271","volume":"30","author":"G Ke","year":"2017","unstructured":"Ke, G. et al. Lightgbm: A highly efficient gradient boosting decision tree. Adv. Neural Inf. Process. Syst. 30, 1271 (2017).","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"49673_CR43","doi-asserted-by":"publisher","DOI":"10.1201\/9781420054989","volume-title":"Polarimetric Radar Imaging: From Basics to Applications","author":"J-S Lee","year":"2017","unstructured":"Lee, J.-S. & Pottier, E. Polarimetric Radar Imaging: From Basics to Applications (CRC Press, 2017)."},{"key":"49673_CR44","doi-asserted-by":"publisher","first-page":"4466","DOI":"10.3390\/jcm12134466","volume":"12","author":"S Akbulut","year":"2023","unstructured":"Akbulut, S. et al. Effect of COVID-19 pandemic on patients who have undergone liver transplantation: Retrospective cohort study. J. Clin. Med. 12, 4466 (2023).","journal-title":"J. Clin. Med."},{"key":"49673_CR45","doi-asserted-by":"publisher","first-page":"1714","DOI":"10.3390\/diagnostics11091714","volume":"11","author":"DD Rufo","year":"2021","unstructured":"Rufo, D. D., Debelee, T. G., Ibenthal, A. & Negera, W. G. Diagnosis of diabetes mellitus using gradient boosting machine (LightGBM). Diagnostics 11, 1714 (2021).","journal-title":"Diagnostics"},{"key":"49673_CR46","doi-asserted-by":"publisher","first-page":"2706","DOI":"10.1109\/TPDS.2019.2920131","volume":"30","author":"Z Wen","year":"2019","unstructured":"Wen, Z. et al. Exploiting GPUs for efficient gradient boosting decision tree training. IEEE Trans. Parallel Distrib. Syst. 30, 2706\u20132717 (2019).","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"49673_CR47","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.ins.2020.05.042","volume":"535","author":"Z Chu","year":"2020","unstructured":"Chu, Z., Yu, J. & Hamdulla, A. LPG-model: A novel model for throughput prediction in stream processing, using a light gradient boosting machine, incremental principal component analysis, and deep gated recurrent unit network. Inf. Sci. 535, 107\u2013129 (2020).","journal-title":"Inf. Sci."},{"key":"49673_CR48","unstructured":"Lou, Y., Caruana, R., Gehrke, J. & Hooker, G. Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 623\u2013631."},{"key":"49673_CR49","doi-asserted-by":"publisher","first-page":"4991","DOI":"10.3390\/rs13244991","volume":"13","author":"AE Maxwell","year":"2021","unstructured":"Maxwell, A. E., Sharma, M. & Donaldson, K. A. Explainable boosting machines for slope failure spatial predictive modeling. Remote Sens. 13, 4991 (2021).","journal-title":"Remote Sens."},{"key":"49673_CR50","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106466","volume":"152","author":"M Liu","year":"2023","unstructured":"Liu, M., Guo, C. & Guo, S. An explainable knowledge distillation method with XGBoost for ICU mortality prediction. Comput. Biol. Med. 152, 106466 (2023).","journal-title":"Comput. Biol. Med."},{"key":"49673_CR51","unstructured":"Nori, H., Jenkins, S., Koch, P. & Caruana, R. Interpretml: A unified framework for machine learning interpretability. arXiv Preprint arXiv:1909.09223 (2019)."},{"key":"49673_CR52","unstructured":"Niculescu-Mizil, A. & Caruana, R. Proceedings of the 22nd International Conference on Machine Learning. 625\u2013632."},{"key":"49673_CR53","unstructured":"Tonekaboni, S., Joshi, S., McCradden, M. D. & Goldenberg, A. Machine Learning for Healthcare Conference. 359\u2013380 (PMLR)."},{"key":"49673_CR54","unstructured":"Zadrozny, B. & Elkan, C. Icml. 609\u2013616."},{"key":"49673_CR55","unstructured":"Zadrozny, B. & Elkan, C. Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 694\u2013699."},{"key":"49673_CR56","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1023\/A:1012470815092","volume":"46","author":"A Demiriz","year":"2002","unstructured":"Demiriz, A., Bennett, K. P. & Shawe-Taylor, J. Linear programming boosting via column generation. Mach. Learn. 46, 225\u2013254 (2002).","journal-title":"Mach. Learn."},{"key":"49673_CR57","first-page":"3","volume":"20","author":"G R\u00e4tsch","year":"2007","unstructured":"R\u00e4tsch, G., Warmuth, M. K. & Glocer, K. Boosting algorithms for maximizing the soft margin. Adv. Neural Inf. Process. Syst. 20, 3\u20136 (2007).","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"49673_CR58","unstructured":"Freund, Y. Proceedings of the Twelfth Annual Conference on Computational Learning Theory. 102\u2013113."},{"key":"49673_CR59","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1214\/aos\/1016218223","volume":"28","author":"J Friedman","year":"2000","unstructured":"Friedman, J., Hastie, T. & Tibshirani, R. Additive logistic regression: A statistical view of boosting (with discussion and a rejoinder by the authors). Ann. Stat. 28, 337\u2013407 (2000).","journal-title":"Ann. Stat."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-023-49673-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-023-49673-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-023-49673-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,5]],"date-time":"2024-11-05T22:42:19Z","timestamp":1730846539000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-023-49673-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,14]]},"references-count":59,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["49673"],"URL":"https:\/\/doi.org\/10.1038\/s41598-023-49673-2","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,14]]},"assertion":[{"value":"12 September 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 December 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 December 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"22189"}}