{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T01:46:44Z","timestamp":1777081604015,"version":"3.51.4"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T00:00:00Z","timestamp":1726444800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T00:00:00Z","timestamp":1726444800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"DOI":"10.1186\/s12911-024-02645-6","type":"journal-article","created":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T11:02:29Z","timestamp":1726484549000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Machine learning-based evaluation of prognostic factors for mortality and relapse in patients with acute lymphoblastic leukemia: a comparative simulation study"],"prefix":"10.1186","volume":"24","author":[{"given":"Zahra","family":"Mehrbakhsh","sequence":"first","affiliation":[]},{"given":"Roghayyeh","family":"Hassanzadeh","sequence":"additional","affiliation":[]},{"given":"Nasser","family":"Behnampour","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4378-3143","authenticated-orcid":false,"given":"Leili","family":"Tapak","sequence":"additional","affiliation":[]},{"given":"Ziba","family":"Zarrin","sequence":"additional","affiliation":[]},{"given":"Salman","family":"Khazaei","sequence":"additional","affiliation":[]},{"given":"Irina","family":"Dinu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,9,16]]},"reference":[{"key":"2645_CR1","unstructured":"World Population Prospects 2023. https:\/\/population.un.org\/wpp."},{"key":"2645_CR2","unstructured":"World Health Organization 2023. https:\/\/www.who.int\/data\/gho\/data\/themes\/topics\/topic-details\/GHO\/child-mortality-and-causes-of-death."},{"issue":"1","key":"2645_CR3","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1289\/ehp.9023","volume":"115","author":"M Belson","year":"2007","unstructured":"Belson M, Kingsley B, Holmes A. Risk factors for acute leukemia in children: a review. Environ Health Perspect. 2007;115(1):138\u201345.","journal-title":"Environ Health Perspect"},{"key":"2645_CR4","doi-asserted-by":"publisher","first-page":"100399","DOI":"10.1016\/j.imu.2020.100399","volume":"20","author":"A Kashef","year":"2020","unstructured":"Kashef A, Khatibi T, Mehrvar A. Treatment outcome classification of pediatric acute lymphoblastic leukemia patients with clinical and medical data using machine learning: a case study at MAHAK hospital. Inf Med Unlocked. 2020;20:100399.","journal-title":"Inf Med Unlocked"},{"issue":"4","key":"2645_CR5","doi-asserted-by":"publisher","first-page":"123","DOI":"10.14740\/jh751","volume":"9","author":"J Torres-Flores","year":"2020","unstructured":"Torres-Flores J, Espinoza-Zamora R, Garcia-Mendez J, Cervera-Ceballos E, Sosa-Espinoza A, Zapata-Canto N. Treatment-related mortality from infectious complications in an acute leukemia clinic. J Hematol. 2020;9(4):123.","journal-title":"J Hematol"},{"issue":"7","key":"2645_CR6","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1542\/pir.2018-0192","volume":"40","author":"JA Kaplan","year":"2019","unstructured":"Kaplan JA. Leukemia in children. Pediatr Rev. 2019;40(7):319\u201331.","journal-title":"Pediatr Rev"},{"issue":"1","key":"2645_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12887-020-02408-y","volume":"20","author":"JS Torres-Roman","year":"2020","unstructured":"Torres-Roman JS, Valcarcel B, Guerra-Canchari P, Santos CAD, Barbosa IR, La Vecchia C, et al. Leukemia mortality in children from Latin America: trends and predictions to 2030. BMC Pediatr. 2020;20(1):1\u20139.","journal-title":"BMC Pediatr"},{"issue":"12","key":"2645_CR8","doi-asserted-by":"publisher","first-page":"2142","DOI":"10.1038\/leu.2008.251","volume":"22","author":"K Nguyen","year":"2008","unstructured":"Nguyen K, Devidas M, Cheng S-C, La M, Raetz EA, Carroll WL, et al. Factors influencing survival after relapse from acute lymphoblastic leukemia: a children\u2019s oncology group study. Leukemia. 2008;22(12):2142\u201350.","journal-title":"Leukemia"},{"issue":"1","key":"2645_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-75860-6","volume":"10","author":"J Zawitkowska","year":"2020","unstructured":"Zawitkowska J, Lejman M, Romiszewski M, Matysiak M, \u0106wikli\u0144ska M, Balwierz W, et al. Results of two consecutive treatment protocols in Polish children with acute lymphoblastic leukemia. Sci Rep. 2020;10(1):1\u20139.","journal-title":"Sci Rep"},{"key":"2645_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11912-020-01009-3","volume":"23","author":"SE Conneely","year":"2021","unstructured":"Conneely SE, Stevens AM. Acute myeloid leukemia in children: emerging paradigms in genetics and new approaches to therapy. Curr Oncol Rep. 2021;23:1\u201313.","journal-title":"Curr Oncol Rep"},{"issue":"1","key":"2645_CR11","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/S0933-3657(02)00086-6","volume":"27","author":"JM Jerez-Aragon\u00e9s","year":"2003","unstructured":"Jerez-Aragon\u00e9s JM, G\u00f3mez-Ruiz JA, Ramos-Jim\u00e9nez G, Mu\u00f1oz-P\u00e9rez J, Alba-Conejo E. A combined neural network and decision trees model for prognosis of breast cancer relapse. Artif Intell Med. 2003;27(1):45\u201363.","journal-title":"Artif Intell Med"},{"issue":"14","key":"2645_CR12","doi-asserted-by":"publisher","first-page":"1347","DOI":"10.1056\/NEJMra1814259","volume":"380","author":"A Rajkomar","year":"2019","unstructured":"Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019;380(14):1347\u201358.","journal-title":"N Engl J Med"},{"issue":"3","key":"2645_CR13","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1007\/s12525-021-00475-2","volume":"31","author":"C Janiesch","year":"2021","unstructured":"Janiesch C, Zschech P, Heinrich K. Machine learning and deep learning. Electron Markets. 2021;31(3):685\u201395.","journal-title":"Electron Markets"},{"key":"2645_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13102-020-00217-5","volume":"12","author":"M Farhadian","year":"2020","unstructured":"Farhadian M, Torkaman S, Mojarad F. Random forest algorithm to identify factors associated with sports-related dental injuries in 6 to 13-year-old athlete children in Hamadan, Iran-2018-a cross-sectional study. BMC Sports Sci Med Rehabilitation. 2020;12:1\u20139.","journal-title":"BMC Sports Sci Med Rehabilitation"},{"key":"2645_CR15","doi-asserted-by":"publisher","first-page":"459","DOI":"10.6000\/1927-5129.2017.13.76","volume":"13","author":"AA Soofi","year":"2017","unstructured":"Soofi AA, Awan A. Classification techniques in machine learning: applications and issues. J Basic Appl Sci. 2017;13:459\u201365.","journal-title":"J Basic Appl Sci"},{"key":"2645_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40779-021-00338-z","volume":"8","author":"W-T Wu","year":"2021","unstructured":"Wu W-T, Li Y-J, Feng A-Z, Li L, Huang T, Xu A-D, et al. Data mining in clinical big data: the frequently used databases, steps, and methodological models. Military Med Res. 2021;8:1\u201312.","journal-title":"Military Med Res"},{"issue":"7639","key":"2645_CR17","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/nature21056","volume":"542","author":"A Esteva","year":"2017","unstructured":"Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115\u20138.","journal-title":"Nature"},{"issue":"4","key":"2645_CR18","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1007\/s42979-023-01825-x","volume":"4","author":"R Karmakar","year":"2023","unstructured":"Karmakar R, Chatterjee S, Das AK, Mandal A. BCPUML: breast cancer prediction using machine learning approach\u2014A performance analysis. SN Comput Sci. 2023;4(4):377.","journal-title":"SN Comput Sci"},{"key":"2645_CR19","doi-asserted-by":"publisher","first-page":"100016","DOI":"10.1016\/j.health.2022.100016","volume":"2","author":"V Chang","year":"2022","unstructured":"Chang V, Bhavani VR, Xu AQ, Hossain M. An artificial intelligence model for heart disease detection using machine learning algorithms. Healthc Analytics. 2022;2:100016.","journal-title":"Healthc Analytics"},{"issue":"1","key":"2645_CR20","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1186\/s12911-022-01939-x","volume":"22","author":"S Moslehi","year":"2022","unstructured":"Moslehi S, Rabiei N, Soltanian AR, Mamani M. Application of machine learning models based on decision trees in classifying the factors affecting mortality of COVID-19 patients in Hamadan, Iran. BMC Med Inf Decis Mak. 2022;22(1):192.","journal-title":"BMC Med Inf Decis Mak"},{"issue":"1","key":"2645_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12874-023-01920-w","volume":"23","author":"R Hassanzadeh","year":"2023","unstructured":"Hassanzadeh R, Farhadian M, Rafieemehr H. Hospital mortality prediction in traumatic injuries patients: comparing different SMOTE-based machine learning algorithms. BMC Med Res Methodol. 2023;23(1):1\u201315.","journal-title":"BMC Med Res Methodol"},{"issue":"1","key":"2645_CR22","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1177\/1932296815620200","volume":"10","author":"JP Anderson","year":"2016","unstructured":"Anderson JP, Parikh JR, Shenfeld DK, Ivanov V, Marks C, Church BW, et al. Reverse engineering and evaluation of prediction models for progression to type 2 diabetes: an application of machine learning using electronic health records. J Diabetes Sci Technol. 2016;10(1):6\u201318.","journal-title":"J Diabetes Sci Technol"},{"key":"2645_CR23","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1016\/j.csbj.2014.11.005","volume":"13","author":"K Kourou","year":"2015","unstructured":"Kourou K, Exarchos TP, Exarchos KP, Karamouzis MV, Fotiadis DI. Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J. 2015;13:8\u201317.","journal-title":"Comput Struct Biotechnol J"},{"key":"2645_CR24","doi-asserted-by":"publisher","first-page":"117693510600200","DOI":"10.1177\/117693510600200030","volume":"2","author":"JA Cruz","year":"2006","unstructured":"Cruz JA, Wishart DS. Applications of machine learning in cancer prediction and prognosis. Cancer Inform. 2006;2:117693510600200030.","journal-title":"Cancer Inform"},{"issue":"2","key":"2645_CR25","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1016\/S1535-6108(02)00032-6","volume":"1","author":"E-J Yeoh","year":"2002","unstructured":"Yeoh E-J, Ross ME, Shurtleff SA, Williams WK, Patel D, Mahfouz R, et al. Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling. Cancer Cell. 2002;1(2):133\u201343.","journal-title":"Cancer Cell"},{"issue":"6","key":"2645_CR26","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1111\/ijlh.13089","volume":"41","author":"HT Salah","year":"2019","unstructured":"Salah HT, Muhsen IN, Salama ME, Owaidah T, Hashmi SK. Machine learning applications in the diagnosis of leukemia: current trends and future directions. Int J Lab Hematol. 2019;41(6):717\u201325.","journal-title":"Int J Lab Hematol"},{"issue":"8","key":"2645_CR27","doi-asserted-by":"publisher","first-page":"2951","DOI":"10.1182\/blood-2003-01-0338","volume":"102","author":"ME Ross","year":"2003","unstructured":"Ross ME, Zhou X, Song G, Shurtleff SA, Girtman K, Williams WK, et al. Classification of pediatric acute lymphoblastic leukemia by gene expression profiling. Blood. 2003;102(8):2951\u20139.","journal-title":"Blood"},{"issue":"7","key":"2645_CR28","doi-asserted-by":"publisher","first-page":"1270","DOI":"10.1038\/sj.leu.2403392","volume":"18","author":"H Willenbrock","year":"2004","unstructured":"Willenbrock H, Juncker A, Schmiegelow K, Knudsen S, Ryder L. Prediction of immunophenotype, treatment response, and relapse in childhood acute lymphoblastic leukemia using DNA microarrays. Leukemia. 2004;18(7):1270\u20137.","journal-title":"Leukemia"},{"key":"2645_CR29","doi-asserted-by":"crossref","unstructured":"Mohapatra S, Patra D, Satpathi S, editors. Image analysis of blood microscopic images for acute leukemia detection. 2010 international conference on industrial electronics, control and robotics; 2010: IEEE.","DOI":"10.1109\/IECR.2010.5720171"},{"key":"2645_CR30","doi-asserted-by":"crossref","unstructured":"Tran V-N, Ismail W, Hassan R, Yoshitaka A, editors. An automated method for the nuclei and cytoplasm of acute myeloid leukemia detection in blood smear images. 2016 World Automation Congress (WAC); 2016: IEEE.","DOI":"10.1109\/WAC.2016.7583023"},{"issue":"23","key":"2645_CR31","doi-asserted-by":"publisher","first-page":"6077","DOI":"10.1182\/bloodadvances.2020002997","volume":"4","author":"J-N Eckardt","year":"2020","unstructured":"Eckardt J-N, Bornh\u00e4user M, Wendt K, Middeke JM. Application of machine learning in the management of acute myeloid leukemia: current practice and future prospects. Blood Adv. 2020;4(23):6077\u201385.","journal-title":"Blood Adv"},{"key":"2645_CR32","first-page":"1","volume":"2021","author":"M Ghaderzadeh","year":"2021","unstructured":"Ghaderzadeh M, Asadi F, Hosseini A, Bashash D, Abolghasemi H, Roshanpour A. Machine learning in detection and classification of leukemia using smear blood images: a systematic review. Sci Program. 2021;2021:1\u201314.","journal-title":"Sci Program"},{"issue":"1","key":"2645_CR33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-016-0028-x","volume":"7","author":"L Pan","year":"2017","unstructured":"Pan L, Liu G, Lin F, Zhong S, Xia H, Sun X, et al. Machine learning applications for prediction of relapse in childhood acute lymphoblastic leukemia. Sci Rep. 2017;7(1):1\u20139.","journal-title":"Sci Rep"},{"issue":"2","key":"2645_CR34","doi-asserted-by":"publisher","first-page":"185","DOI":"10.3390\/rs11020185","volume":"11","author":"A Ramezan","year":"2019","unstructured":"Ramezan A, Warner CA, Maxwell TE. Evaluation of sampling and cross-validation tuning strategies for regional-scale machine learning classification. Remote Sens. 2019;11(2):185.","journal-title":"Remote Sens"},{"issue":"3","key":"2645_CR35","doi-asserted-by":"publisher","first-page":"189","DOI":"10.4258\/hir.2021.27.3.189","volume":"27","author":"I Tougui","year":"2021","unstructured":"Tougui I, Jilbab A, El Mhamdi J. Impact of the choice of cross-validation techniques on the results of machine learning-based diagnostic applications. Healthc Inf Res. 2021;27(3):189\u201399.","journal-title":"Healthc Inf Res"},{"key":"2645_CR36","doi-asserted-by":"crossref","unstructured":"Agresti A, Kateri M. Categorical data analysis. Springer; 2011.","DOI":"10.1007\/978-3-642-04898-2_161"},{"issue":"1","key":"2645_CR37","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1007\/s00357-006-0007-1","volume":"23","author":"SK Lee","year":"2006","unstructured":"Lee SK. On classification and regression trees for multiple responses and its application. J Classif. 2006;23(1):123\u201341.","journal-title":"J Classif"},{"issue":"1","key":"2645_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13011-019-0242-1","volume":"14","author":"S Najafi-Ghobadi","year":"2019","unstructured":"Najafi-Ghobadi S, Najafi-Ghobadi K, Tapak L, Aghaei A. Application of data mining techniques and logistic regression to model drug use transition to injection: a case study in drug use treatment centers in Kermanshah Province, Iran. Subst Abuse Treat Prev Policy. 2019;14(1):1\u201311.","journal-title":"Subst Abuse Treat Prev Policy"},{"key":"2645_CR39","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1007\/BF00994006","volume":"8","author":"W Buntine","year":"1992","unstructured":"Buntine W, Niblett T. A further comparison of splitting rules for decision-tree induction. Mach Learn. 1992;8:75\u201385.","journal-title":"Mach Learn"},{"issue":"4","key":"2645_CR40","doi-asserted-by":"publisher","first-page":"307","DOI":"10.4258\/hir.2021.27.4.307","volume":"27","author":"R Najafi-Vosough","year":"2021","unstructured":"Najafi-Vosough R, Faradmal J, Hosseini SK, Moghimbeigi A, Mahjub H. Predicting hospital readmission in heart failure patients in Iran: a comparison of various machine learning methods. Healthc Inf Res. 2021;27(4):307\u201314.","journal-title":"Healthc Inf Res"},{"key":"2645_CR41","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L. Random forests. Mach Learn. 2001;45:5\u201332.","journal-title":"Mach Learn"},{"issue":"1\u20134","key":"2645_CR42","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/S0925-2312(01)00644-0","volume":"48","author":"JA Suykens","year":"2002","unstructured":"Suykens JA, De Brabanter J, Lukas L, Vandewalle J. Weighted least squares support vector machines: robustness and sparse approximation. Neurocomputing. 2002;48(1\u20134):85\u2013105.","journal-title":"Neurocomputing"},{"key":"2645_CR43","doi-asserted-by":"publisher","first-page":"110086","DOI":"10.1016\/j.chaos.2020.110086","volume":"139","author":"S Singh","year":"2020","unstructured":"Singh S, Parmar KS, Makkhan SJS, Kaur J, Peshoria S, Kumar J. Study of ARIMA and least square support vector machine (LS-SVM) models for the prediction of SARS-CoV-2 confirmed cases in the most affected countries. Chaos Solitons Fractals. 2020;139:110086.","journal-title":"Chaos Solitons Fractals"},{"key":"2645_CR44","doi-asserted-by":"crossref","unstructured":"Hastie T, Tibshirani R, Friedman JH, Friedman JH. The elements of statistical learning: data mining, inference, and prediction. Springer; 2009.","DOI":"10.1007\/978-0-387-84858-7"},{"key":"2645_CR45","doi-asserted-by":"crossref","unstructured":"Ray S, editor. A quick review of machine learning algorithms. 2019 International conference on machine learning, big data, cloud and parallel computing (COMITCon); 2019: IEEE.","DOI":"10.1109\/COMITCon.2019.8862451"},{"key":"2645_CR46","unstructured":"Garson DG. Interpreting neural network connection weights. 1991."},{"issue":"3","key":"2645_CR47","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1016\/j.cegh.2018.10.003","volume":"7","author":"L Tapak","year":"2019","unstructured":"Tapak L, Shirmohammadi-Khorram N, Amini P, Alafchi B, Hamidi O, Poorolajal J. Prediction of survival and metastasis in breast cancer patients using machine learning classifiers. Clin Epidemiol Global Health. 2019;7(3):293\u20139.","journal-title":"Clin Epidemiol Global Health"},{"issue":"06","key":"2645_CR48","doi-asserted-by":"publisher","first-page":"419","DOI":"10.3414\/ME13-01-0122","volume":"53","author":"A Mayr","year":"2014","unstructured":"Mayr A, Binder H, Gefeller O, Schmid M. The evolution of boosting algorithms. Methods Inf Med. 2014;53(06):419\u201327.","journal-title":"Methods Inf Med"},{"issue":"1","key":"2645_CR49","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1186\/s12911-022-01823-8","volume":"22","author":"S Shariatnia","year":"2022","unstructured":"Shariatnia S, Ziaratban M, Rajabi A, Salehi A, Abdi Zarrini K, Vakili M. Modeling the diagnosis of coronary artery disease by discriminant analysis and logistic regression: a cross-sectional study. BMC Med Inf Decis Mak. 2022;22(1):85.","journal-title":"BMC Med Inf Decis Mak"},{"key":"2645_CR50","doi-asserted-by":"crossref","unstructured":"Izenman AJ. Linear discriminant analysis. Modern multivariate statistical techniques: regression, classification, and manifold learning. Springer; 2013. pp. 237\u201380.","DOI":"10.1007\/978-0-387-78189-1_8"},{"issue":"2","key":"2645_CR51","first-page":"627","volume":"4","author":"K Hajian-Tilaki","year":"2013","unstructured":"Hajian-Tilaki K. Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation. Caspian J Intern Med. 2013;4(2):627.","journal-title":"Caspian J Intern Med"},{"issue":"27","key":"2645_CR52","doi-asserted-by":"publisher","first-page":"4376","DOI":"10.1200\/JCO.2007.14.4519","volume":"26","author":"D Bhojwani","year":"2008","unstructured":"Bhojwani D, Kang H, Menezes RX, Yang W, Sather H, Moskowitz NP, et al. Gene expression signatures predictive of early response and outcome in high-risk childhood acute lymphoblastic leukemia: a children\u2019s oncology group study. J Clin Oncol. 2008;26(27):4376.","journal-title":"J Clin Oncol"},{"issue":"14","key":"2645_CR53","doi-asserted-by":"publisher","first-page":"1663","DOI":"10.1200\/JCO.2011.37.8018","volume":"30","author":"SP Hunger","year":"2012","unstructured":"Hunger SP, Lu X, Devidas M, Camitta BM, Gaynon PS, Winick NJ, et al. Improved survival for children and adolescents with acute lymphoblastic leukemia between 1990 and 2005: a report from the children\u2019s oncology group. J Clin Oncol. 2012;30(14):1663.","journal-title":"J Clin Oncol"},{"issue":"3","key":"2645_CR54","doi-asserted-by":"publisher","first-page":"926","DOI":"10.1182\/blood-2006-01-024729","volume":"109","author":"KR Schultz","year":"2007","unstructured":"Schultz KR, Pullen DJ, Sather HN, Shuster JJ, Devidas M, Borowitz MJ, et al. Risk-and response-based classification of childhood B-precursor acute lymphoblastic leukemia: a combined analysis of prognostic markers from the pediatric oncology group (POG) and children\u2019s cancer group (CCG). Blood. 2007;109(3):926\u201335.","journal-title":"Blood"},{"issue":"5","key":"2645_CR55","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1200\/JCO.2010.30.7405","volume":"29","author":"C-H Pui","year":"2011","unstructured":"Pui C-H, Carroll WL, Meshinchi S, Arceci RJ. Biology, risk stratification, and therapy of pediatric acute leukemias: an update. J Clin Oncol. 2011;29(5):551.","journal-title":"J Clin Oncol"},{"issue":"1","key":"2645_CR56","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1186\/s12859-023-05156-9","volume":"24","author":"D Rajput","year":"2023","unstructured":"Rajput D, Wang W-J, Chen C-C. Evaluation of a decided sample size in machine learning applications. BMC Bioinformatics. 2023;24(1):48.","journal-title":"BMC Bioinformatics"},{"key":"2645_CR57","doi-asserted-by":"crossref","unstructured":"Yang Y, Su X, Zhao B, Li G, Hu P, Zhang J et al. Fuzzy-based deep attributed graph clustering. IEEE Trans Fuzzy Syst. 2023.","DOI":"10.1109\/TFUZZ.2023.3338565"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-024-02645-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-024-02645-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-024-02645-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,18]],"date-time":"2024-09-18T12:29:28Z","timestamp":1726662568000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-024-02645-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,16]]},"references-count":57,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["2645"],"URL":"https:\/\/doi.org\/10.1186\/s12911-024-02645-6","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,16]]},"assertion":[{"value":"6 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 August 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 September 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":"This study was submitted to and approved by the Ethical Committee of Hamadan University of Medical Science (Ethical code: IR.UMSHA.REC.1402.748). Informed consent was obtained from a parent or legal guardian for being included in the study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"261"}}