{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T07:11:32Z","timestamp":1792653092277,"version":"4.5.0"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T00:00:00Z","timestamp":1710288000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T00:00:00Z","timestamp":1710288000000},"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":["Int J Comput Intell Syst"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Breast cancer has been one of the leading causes of death among women in the world. Early detection of this disease can save patient\u2019s lives and reduce mortality. Due to the large number of features involved in the diagnosis of this disease, the breast cancer diagnosis process can be time consuming. To reduce cost and time and improving accuracy of breast cancer diagnosis, this paper propose a feature selection algorithm based on particle swarm optimization (PSO) combined with machine learning methods for selection the most effective features for breast cancer diagnosis among all features. In order to evaluate the efficiency of the proposed feature selection method, it was tested on three most common breast cancer datasets available in the University of California, Irvine (UCI) repository named: Coimbra dataset (CD), Wisconsin Diagnostic Breast Cancer dataset (WDBC) and Wisconsin Prognostic Breast Cancer dataset (WPBC). In the Coimbra dataset with all its 9 features and without PSO feature selection algorithm the highest obtained accuracy was 87% by Support Vector Machine method, while with PSO feature selection algorithm the accuracy reached to 91% and the number of features was reduced from 9 to 4. In the WDBC dataset with all its 30 features and without PSO feature selection algorithm the highest obtained accuracy was 99% by Random Forest method, while with PSO feature selection algorithm the accuracy reached to 100% and the number of features was reduced from 30 to 19. In the WPBC dataset with all its 33 features and without PSO feature selection algorithm the highest obtained accuracy was 94% by Support Vector Machine method, while with PSO feature selection algorithm the accuracy reached to 96% and the number of features was reduced from 33 to 17. The results of this paper indicated that the proposed feature selection algorithm based on PSO algorithm can improve the accuracy of breast cancer diagnosis. While it has selected fewer and more effective features than the total number of features in the original datasets.<\/jats:p>","DOI":"10.1007\/s44196-024-00428-5","type":"journal-article","created":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T20:24:33Z","timestamp":1710361473000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Improving Breast Cancer Diagnosis Accuracy by Particle Swarm Optimization Feature Selection"],"prefix":"10.1007","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1489-1516","authenticated-orcid":false,"given":"Reihane","family":"Kazerani","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,3,13]]},"reference":[{"issue":"2","key":"428_CR1","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1016\/j.eij.2022.01.002","volume":"23","author":"A Salem","year":"2022","unstructured":"Salem, A., Sherif, A.S., Hussein, M.K.: Efficient framework for detecting COVID-19 and pneumonia from chest X-ray using deep convolutional network. Egypt. Inform. J. 23(2), 247\u2013257 (2022). https:\/\/doi.org\/10.1016\/j.eij.2022.01.002","journal-title":"Egypt. Inform. J."},{"key":"428_CR2","doi-asserted-by":"publisher","first-page":"43","DOI":"10.7314\/APJCP.2016.17.S3.43","volume":"17","author":"H Salehiniya","year":"2016","unstructured":"Salehiniya, H., Ghoncheh, M., Pournamdar, Z.: Incidence and mortality and epidemiology of breast cancer in the world. Asian Pac. J. Cancer Prev. 17, 43\u201346 (2016)","journal-title":"Asian Pac. J. Cancer Prev."},{"issue":"6","key":"428_CR3","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1016\/j.ijsu.2010.05.012","volume":"8","author":"H Najjar","year":"2010","unstructured":"Najjar, H., Easson, A.: Age at diagnosis of breast cancer in Arab nations. Int. J. Surg. 8(6), 448\u2013452 (2010). https:\/\/doi.org\/10.1016\/j.ijsu.2010.05.012","journal-title":"Int. J. Surg."},{"issue":"4","key":"428_CR4","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1016\/j.ijsu.2006.06.015","volume":"5","author":"NS El Saghir","year":"2007","unstructured":"El Saghir, N.S., et al.: Trends in epidemiology and management of breast cancer in developing Arab countries: a literature and registry analysis. Int. J. Surg. 5(4), 225\u2013233 (2007). https:\/\/doi.org\/10.1016\/j.ijsu.2006.06.015","journal-title":"Int. J. Surg."},{"key":"428_CR5","doi-asserted-by":"publisher","first-page":"2775","DOI":"10.1109\/ACCESS.2022.3140289","volume":"10","author":"AS Musallam","year":"2022","unstructured":"Musallam, A.S., Sherif, A.S.: A new convolutional neural network architecture for automatic detection of brain tumors in magnetic resonance imaging images. IEEE Access 10, 2775\u20132782 (2022). https:\/\/doi.org\/10.1109\/ACCESS.2022.3140289","journal-title":"IEEE Access"},{"key":"428_CR6","doi-asserted-by":"publisher","unstructured":"Chakradeo, K., Vyawahare, S., Pawar, P.: Breast cancer recurrence prediction using machine learning. In: 2019 IEEE Conf. Inf. Commun. Technol. CICT 2019, pp. 1\u20137 (2019). https:\/\/doi.org\/10.1109\/CICT48419.2019.9066248","DOI":"10.1109\/CICT48419.2019.9066248"},{"key":"428_CR7","doi-asserted-by":"publisher","DOI":"10.1177\/1550147720971505","author":"Vijayalakshmi","year":"2020","unstructured":"Vijayalakshmi, et al.: Multi-modal prediction of breast cancer using particle swarm optimization with non-dominating sorting. Int. J. Distrib. Sens. Netw. (2020). https:\/\/doi.org\/10.1177\/1550147720971505","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"428_CR8","doi-asserted-by":"publisher","first-page":"29637","DOI":"10.1109\/ACCESS.2018.2843443","volume":"6","author":"SB Sakri","year":"2018","unstructured":"Sakri, S.B., Abdul Rashid, N.B., Muhammad Zain, Z.: particle swarm optimization feature selection for breast cancer recurrence prediction. IEEE Access 6, 29637\u201329647 (2018). https:\/\/doi.org\/10.1109\/ACCESS.2018.2843443","journal-title":"IEEE Access"},{"key":"428_CR9","doi-asserted-by":"publisher","unstructured":"Nurhayati, F., Agustian, Lubis, M.D.I.: Particle swarm optimization feature selection for breast cancer prediction. In: 2020 8th Int. Conf. Cyber IT Serv. Manag. CITSM 2020, pp. 5\u201310 (2020). https:\/\/doi.org\/10.1109\/CITSM50537.2020.9268865","DOI":"10.1109\/CITSM50537.2020.9268865"},{"key":"428_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.irbm.2019.05.007","volume":"1","author":"SB Akben","year":"2019","unstructured":"Akben, S.B.: Determination of the blood, hormone and obesity value ranges that indicate the breast cancer, using data mining based expert system. IRBM 1, 1\u20136 (2019). https:\/\/doi.org\/10.1016\/j.irbm.2019.05.007","journal-title":"IRBM"},{"key":"428_CR11","doi-asserted-by":"publisher","unstructured":"Kennedy, J.: Particle swarm: social adaptation of knowledge. In: Proc. IEEE Conf. Evol. Comput. ICEC, pp. 303\u2013308 (1997). https:\/\/doi.org\/10.1109\/icec.1997.592326","DOI":"10.1109\/icec.1997.592326"},{"key":"428_CR12","doi-asserted-by":"publisher","unstructured":"Mafarja M., Sabar, N.R.: Rank based binary particle swarm optimisation for feature selection in classification. In: ACM Int. Conf. Proceeding Ser., no. June (2018). https:\/\/doi.org\/10.1145\/3231053.3231072","DOI":"10.1145\/3231053.3231072"},{"key":"428_CR13","doi-asserted-by":"publisher","unstructured":"Silva, S., Anuncia\u00e7\u00e3o, O., Lotz, M.: A comparison of machine learning methods for the prediction of breast cancer. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6623 LNCS, pp. 159\u2013170 (2011). https:\/\/doi.org\/10.1007\/978-3-642-20389-3_17","DOI":"10.1007\/978-3-642-20389-3_17"},{"issue":"1","key":"428_CR14","doi-asserted-by":"publisher","first-page":"466","DOI":"10.3390\/make1010028","volume":"1","author":"V Silva Ara\u00fajo","year":"2019","unstructured":"Silva Ara\u00fajo, V., Guimar\u00e3es, A., de Campos Souza, P., Silva Rezende, T., Souza Ara\u00fajo, V.: Using resistin, glucose, age and bmi and pruning fuzzy neural network for the construction of expert systems in the prediction of breast cancer. Mach. Learn. Knowl. Extr. 1(1), 466\u2013482 (2019). https:\/\/doi.org\/10.3390\/make1010028","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"428_CR15","doi-asserted-by":"publisher","DOI":"10.18535\/ijecs\/v6i5.31","author":"YA Malla","year":"2017","unstructured":"Malla, Y.A.: A machine learning approach for early prediction of breast cancer. Int. J. Eng. Comput. Sci. (2017). https:\/\/doi.org\/10.18535\/ijecs\/v6i5.31","journal-title":"Int. J. Eng. Comput. Sci."},{"key":"428_CR16","doi-asserted-by":"publisher","DOI":"10.1186\/s12885-017-3877-1","author":"M Patr\u00edcio","year":"2018","unstructured":"Patr\u00edcio, M., et al.: Using resistin, glucose, age and BMI to predict the presence of breast cancer. BMC Cancer (2018). https:\/\/doi.org\/10.1186\/s12885-017-3877-1","journal-title":"BMC Cancer"},{"issue":"January 2019","key":"428_CR17","doi-asserted-by":"publisher","first-page":"10715","DOI":"10.1039\/b000000x","volume":"3","author":"F Paquin","year":"2018","unstructured":"Paquin, F., Rivnay, J., Salleo, A., Stingelin, N., Silva, C.: Breast cancer diagnosis by different machine learning methods using blood analysis data. J. Mater. Chem. C 3(January 2019), 10715\u201310722 (2018). https:\/\/doi.org\/10.1039\/b000000x","journal-title":"J. Mater. Chem. C"},{"issue":"2","key":"428_CR18","doi-asserted-by":"publisher","first-page":"687","DOI":"10.1016\/j.ejor.2017.12.001","volume":"267","author":"H Wang","year":"2018","unstructured":"Wang, H., Zheng, B., Yoon, S.W., Ko, H.S.: A support vector machine-based ensemble algorithm for breast cancer diagnosis. Eur. J. Oper. Res. 267(2), 687\u2013699 (2018). https:\/\/doi.org\/10.1016\/j.ejor.2017.12.001","journal-title":"Eur. J. Oper. Res."},{"key":"428_CR19","unstructured":"2013 UCI: UC Irvine machine learning repository (2013)"},{"key":"428_CR20","unstructured":"UCI machine learning repository, breast cancer Coimbra data set (2018). https:\/\/archive.ics.uci.edu\/ml\/datasets\/Breast+Cancer+Coimbra."},{"issue":"1","key":"428_CR21","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1007\/s10489-007-0073-z","volume":"30","author":"I Maglogiannis","year":"2007","unstructured":"Maglogiannis, I., Zafiropoulos, E., Anagnostopoulos, I.: An intelligent system for automated breast cancer diagnosis and prognosis using SVM based classifiers. Appl. Intell. 30(1), 24\u201336 (2007). https:\/\/doi.org\/10.1007\/s10489-007-0073-z","journal-title":"Appl. Intell."},{"key":"428_CR22","unstructured":"UCI machine learning repository, Breast Cancer Wisconsin (Diagnostic) data set (1995). https:\/\/archive.ics.uci.edu\/ml\/datasets\/Breast+Cancer+Wisconsin+(Diagnostic)."},{"issue":"23","key":"428_CR23","doi-asserted-by":"publisher","first-page":"9193","DOI":"10.1073\/pnas.87.23.9193","volume":"87","author":"WH Wolberg","year":"1990","unstructured":"Wolberg, W.H., Mangasarian, O.L.: Multisurface method of pattern separation for medical diagnosis applied to breast cytology. Proc. Natl. Acad. Sci. U.S.A. 87(23), 9193\u20139196 (1990). https:\/\/doi.org\/10.1073\/pnas.87.23.9193","journal-title":"Proc. Natl. Acad. Sci. U.S.A."},{"key":"428_CR24","unstructured":"Breast Cancer Wisconsin (Prognostic) Data Set (1995). https:\/\/archive.ics.uci.edu\/ml\/datasets\/Breast+Cancer+Wisconsin+(Prognostic)."},{"key":"428_CR25","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1007\/3-540-59119-2_166","volume":"904","author":"Y Freund","year":"1995","unstructured":"Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. Lect. Not. Comput. Sci. (including Subser. Lect. Not. Artif. Intell. Lect. Not. Bioinform.) 904, 23\u201337 (1995). https:\/\/doi.org\/10.1007\/3-540-59119-2_166","journal-title":"Lect. Not. Comput. Sci. (including Subser. Lect. Not. Artif. Intell. Lect. Not. Bioinform.)"},{"key":"428_CR26","unstructured":"Schapire, R.E.: Random Forests. 2001 Kluwer Acad. Publ. Manuf. Netherlands., vol. 2012, no. 4, pp. 1\u20137 (2001). http:\/\/ci.nii.ac.jp\/naid\/110009490689\/"},{"key":"428_CR27","doi-asserted-by":"publisher","first-page":"84","DOI":"10.4018\/978-1-60960-557-5.ch006","volume":"3","author":"EG Galitskaya","year":"2013","unstructured":"Galitskaya, E.G., Galitskkiy, E.B.: Classification trees. Sotsiologicheskie Issled. 3, 84\u201388 (2013). https:\/\/doi.org\/10.4018\/978-1-60960-557-5.ch006","journal-title":"Sotsiologicheskie Issled."},{"key":"428_CR28","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/978-3-642-38652-7","volume":"51","author":"O Kramer","year":"2013","unstructured":"Kramer, O.: Dimensionality reduction with unsupervised nearest neighbors. Intell. Syst. Ref. Libr. 51, 13\u201323 (2013). https:\/\/doi.org\/10.1007\/978-3-642-38652-7","journal-title":"Intell. Syst. Ref. Libr."},{"key":"428_CR29","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1007\/978-1-4419-9878-1","volume-title":"Robust Data Mining","author":"P Xanthopoulos","year":"2013","unstructured":"Xanthopoulos, P., Pardalos, P.M., Trafalis, T.B.: Robust Data Mining, pp. 27\u201333. Springer, New York (2013). https:\/\/doi.org\/10.1007\/978-1-4419-9878-1"},{"key":"428_CR30","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1007\/978-1-4842-3564-5","volume-title":"Pro Machine Learning Algorithms","author":"VK Ayyadevar","year":"2018","unstructured":"Ayyadevar, V.K.: Pro Machine Learning Algorithms, pp. 49\u201369. Apress, Berkeley (2018). https:\/\/doi.org\/10.1007\/978-1-4842-3564-5"},{"issue":"11","key":"428_CR31","doi-asserted-by":"publisher","first-page":"1083","DOI":"10.1080\/07408170500232784","volume":"37","author":"AC Rencher","year":"2005","unstructured":"Rencher, A.C.: A review of \u201cmethods of multivariate analysis, second edition.\u201d IIE Trans. 37(11), 1083\u20131085 (2005)","journal-title":"IIE Trans."},{"key":"428_CR32","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1016\/j.sigpro.2017.11.018","volume":"145","author":"Y Zhu","year":"2018","unstructured":"Zhu, Y., Zhu, C., Li, X.: Improved principal component analysis and linear regression classification for face recognition. Signal Process. 145, 175\u2013182 (2018). https:\/\/doi.org\/10.1016\/j.sigpro.2017.11.018","journal-title":"Signal Process."},{"key":"428_CR33","unstructured":"Webb, G.I.: Na\u00efve Bayes. Encycl. Mach. Learn., vol. \uad6d\ub0b4\uc11d\uc0ac\ud559\uc704\ub17c\ubb38 (2012). http:\/\/www.riss.kr\/link?id=T12754991."},{"key":"428_CR34","volume-title":"Artificial Neural Networks","author":"H Cartwright","year":"2014","unstructured":"Cartwright, H.: Artificial Neural Networks, 2nd edn. Springer, New York (2014)","edition":"2"},{"key":"428_CR35","volume-title":"Neural Networks","author":"G Dreyfus","year":"2005","unstructured":"Dreyfus, G.: Neural Networks. Springer, Berlin (2005)"},{"issue":"2\u20134","key":"428_CR36","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1016\/j.ecolmodel.2007.05.011","volume":"207","author":"J Peters","year":"2007","unstructured":"Peters, J., et al.: Random forests as a tool for ecohydrological distribution modelling. Ecol. Modell. 207(2\u20134), 304\u2013318 (2007). https:\/\/doi.org\/10.1016\/j.ecolmodel.2007.05.011","journal-title":"Ecol. Modell."},{"key":"428_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2014.03.128","author":"O Abu","year":"2014","unstructured":"Abu, O., Abo-hammour, Z.: Numerical solution of systems of second-order boundary value problems using continuous genetic algorithm. Inf. Sci. (NY) (2014). https:\/\/doi.org\/10.1016\/j.ins.2014.03.128","journal-title":"Inf. Sci. (NY)"},{"issue":"1","key":"428_CR38","first-page":"401696","volume":"2014","author":"Z Abo-hammour","year":"2014","unstructured":"Abo-hammour, Z., Arqub, O.A., Momani, S., Shawagfeh, N.: Optimization solution of Troesch\u2019s and Bratu\u2019s problems of ordinary type using novel continuous genetic algorithm. Discrete Dyn. Nat. Soc. 2014(1), 401696 (2014)","journal-title":"Discrete Dyn. Nat. Soc."},{"key":"428_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2013\/831657","volume":"2013","author":"Z Abo-hammour","year":"2013","unstructured":"Abo-hammour, Z., Alsmadi, O., Momani, S., Arqub, O.A.: A genetic algorithm approach for prediction of linear dynamical systems. Math. Probl. Eng. 2013, 1\u201312 (2013)","journal-title":"Math. Probl. Eng."},{"key":"428_CR40","doi-asserted-by":"publisher","DOI":"10.1155\/2012\/205391","author":"OA Arqub","year":"2012","unstructured":"Arqub, O.A., Abo-hammour, Z., Momani, S., Shawagfeh, N.: Solving singular two-point boundary value problems using continuous genetic algorithm. Abstr. Appl. Anal. (2012). https:\/\/doi.org\/10.1155\/2012\/205391","journal-title":"Abstr. Appl. Anal."},{"key":"428_CR41","first-page":"1","volume-title":"Machine Learning Fundamentals: Use Python and Scikit-Learn to Get Up and Running with the Hot-Test Developments in Machine Learning","author":"H Saleh","year":"2018","unstructured":"Saleh, H.: Machine Learning Fundamentals: Use Python and Scikit-Learn to Get Up and Running with the Hot-Test Developments in Machine Learning, pp. 1\u201337. Packt Publishing, Birmingham (2018)"},{"key":"428_CR42","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/j.patrec.2018.11.004","volume":"132","author":"M Abdar","year":"2020","unstructured":"Abdar, M., et al.: A new nested ensemble technique for automated diagnosis of breast cancer. Pattern Recognit. Lett. 132, 123\u2013131 (2020). https:\/\/doi.org\/10.1016\/j.patrec.2018.11.004","journal-title":"Pattern Recognit. Lett."},{"key":"428_CR43","unstructured":"KarlRosaen: K-fold cross-validation (2016). http:\/\/karlrosaen.com\/ml\/learning-log\/2016-06-20\/."},{"key":"428_CR44","doi-asserted-by":"publisher","unstructured":"Zhou, Z.H.: Cost-sensitive learning. In: Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 6820 LNAI, pp. 17\u201318 (2011) https:\/\/doi.org\/10.1007\/978-3-642-22589-5_2.","DOI":"10.1007\/978-3-642-22589-5_2"},{"issue":"4","key":"428_CR45","doi-asserted-by":"publisher","first-page":"212","DOI":"10.11648\/j.acm.20180704.15","volume":"7","author":"Y Li","year":"2018","unstructured":"Li, Y.: Performance evaluation of machine learning methods for breast cancer prediction. Appl. Comput. Math. 7(4), 212 (2018). https:\/\/doi.org\/10.11648\/j.acm.20180704.15","journal-title":"Appl. Comput. Math."}],"container-title":["International Journal of Computational Intelligence Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-024-00428-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44196-024-00428-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-024-00428-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T20:33:14Z","timestamp":1710361994000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44196-024-00428-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,13]]},"references-count":45,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["428"],"URL":"https:\/\/doi.org\/10.1007\/s44196-024-00428-5","relation":{},"ISSN":["1875-6883"],"issn-type":[{"value":"1875-6883","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,13]]},"assertion":[{"value":"23 July 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 January 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 March 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The author declares that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The author agrees the article to be published.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"44"}}