{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T19:46:38Z","timestamp":1774467998835,"version":"3.50.1"},"reference-count":69,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,11,7]],"date-time":"2019-11-07T00:00:00Z","timestamp":1573084800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2019,11,7]],"date-time":"2019-11-07T00:00:00Z","timestamp":1573084800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"published-print":{"date-parts":[[2019,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>The hypochromic microcytic anemia (HMA) commonly found in Thailand are iron deficiency anemia (IDA) and thalassemia trait (TT). Accurate discrimination between IDA and TT is an important issue and better methods are urgently needed. Although considerable RBC formulas and indices with various optimal cut-off values have been developed, distinguishing between IDA and TT is still a challenging problem due to the diversity of various anemic populations. To address this problem, it is desirable to develop an improved and automated prediction model for discriminating IDA from TT.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We retrospectively collected laboratory data of HMA found in Thai adults. Five machine learnings, including<jats:italic>k<\/jats:italic>-nearest neighbor (<jats:italic>k<\/jats:italic>-NN), decision tree, random forest (RF), artificial neural network (ANN) and support vector machine (SVM), were applied to construct a discriminant model. Performance was assessed and compared with thirteen existing discriminant formulas and indices.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The data of 186 patients (146 patients with TT and 40 with IDA) were enrolled. The interpretable rules derived from the RF model were proposed to demonstrate the combination of RBC indices for discriminating IDA from TT. A web-based tool \u2018ThalPred\u2019 was implemented using an SVM model based on seven RBC parameters. ThalPred achieved prediction results with an external accuracy, MCC and AUC of 95.59, 0.87 and 0.98, respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>ThalPred and an interpretable rule were provided for distinguishing IDA from TT. For the convenience of health care team experimental scientists, a web-based tool has been established at<jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"http:\/\/codes.bio\/cryoprotect\/\">http:\/\/codes.bio\/thalpred\/<\/jats:ext-link>by which users can easily get their desired screening test result without the need to go through the underlying mathematical and computational details.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12911-019-0929-2","type":"journal-article","created":{"date-parts":[[2019,11,7]],"date-time":"2019-11-07T11:05:56Z","timestamp":1573124756000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":53,"title":["ThalPred: a web-based prediction tool for discriminating thalassemia trait and iron deficiency anemia"],"prefix":"10.1186","volume":"19","author":[{"given":"V.","family":"Laengsri","sequence":"first","affiliation":[]},{"given":"W.","family":"Shoombuatong","sequence":"additional","affiliation":[]},{"given":"W.","family":"Adirojananon","sequence":"additional","affiliation":[]},{"given":"C.","family":"Nantasenamat","sequence":"additional","affiliation":[]},{"given":"V.","family":"Prachayasittikul","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1123-1037","authenticated-orcid":false,"given":"P.","family":"Nuchnoi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,11,7]]},"reference":[{"key":"929_CR1","doi-asserted-by":"publisher","first-page":"615","DOI":"10.1182\/blood-2013-06-508325","volume":"123","author":"NJ Kassebaum","year":"2014","unstructured":"Kassebaum NJ, Jasrasaria R, Naghavi M, Wulf SK, Johns N, Lozano R, et al. A systematic analysis of global anemia burden from 1990 to 2010. Blood. 2014;123:615\u201324. https:\/\/doi.org\/10.1182\/blood-2013-06-508325 .","journal-title":"Blood"},{"issue":"4 Suppl","key":"929_CR2","doi-asserted-by":"publisher","first-page":"862S","DOI":"10.1093\/jn\/132.4.862S","volume":"132","author":"P Winichagoon","year":"2002","unstructured":"Winichagoon P. Prevention and control of anemia: Thailand experiences. J Nutr. 2002;132(4 Suppl):862S\u20136S.","journal-title":"J Nutr"},{"key":"929_CR3","doi-asserted-by":"crossref","first-page":"4","DOI":"10.47102\/annals-acadmedsg.V46N1p4","volume":"46","author":"XY Thong","year":"2017","unstructured":"Thong XY, Lee LY, Chia DA, Wong YC, Biswas A. Management and outcomes of fetal hydrops in a tertiary care centre in singapore. Ann Acad Med Singap. 2017;46:4\u201310.","journal-title":"Ann Acad Med Singap"},{"key":"929_CR4","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1177\/1756283X11398736","volume":"4","author":"TD Johnson-Wimbley","year":"2011","unstructured":"Johnson-Wimbley TD, Graham DY. Diagnosis and management of iron deficiency anemia in the 21st century. Therap Adv Gastroenterol. 2011;4:177\u201384. https:\/\/doi.org\/10.1177\/1756283X11398736 .","journal-title":"Therap Adv Gastroenterol"},{"key":"929_CR5","doi-asserted-by":"publisher","first-page":"274","DOI":"10.7205\/MILMED.169.4.274","volume":"169","author":"O Nathalang","year":"2004","unstructured":"Nathalang O, Arnutti P, Nillakupt K. Thalassemia screening among Royal Thai Army medical cadets. Mil Med. 2004;169:274\u20136. https:\/\/doi.org\/10.7205\/MILMED.169.4.274 .","journal-title":"Mil Med"},{"issue":"Suppl 3","key":"929_CR6","first-page":"S35","volume":"88","author":"O Nathalang","year":"2005","unstructured":"Nathalang O, Nillakupt K, Arnutti P, Boonsiri T, Panichkul S, Areekul W. Screening for thalassemia and hemoglobinopathy in a rural area of Thailand: a preliminary study. J Med Assoc Thai. 2005;88(Suppl 3):S35\u201342.","journal-title":"J Med Assoc Thai"},{"key":"929_CR7","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1620\/tjem.223.223","volume":"223","author":"Z Ou","year":"2011","unstructured":"Ou Z, Li Q, Liu W, Sun X. Elevated hemoglobin A2 as a marker for \u03b2-thalassemia trait in pregnant women. Tohoku J Exp Med. 2011;223:223\u20136.","journal-title":"Tohoku J Exp Med"},{"key":"929_CR8","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1182\/blood.V53.2.288.bloodjournal532288","volume":"53","author":"JD Bessman","year":"1979","unstructured":"Bessman JD, Feinstein DI. Quantitative anisocytosis as a discriminant between iron deficiency and thalassemia minor. Blood. 1979;53:288\u201393.","journal-title":"Blood"},{"key":"929_CR9","doi-asserted-by":"publisher","first-page":"473","DOI":"10.3923\/pjbs.2009.473.475","volume":"12","author":"MA Ehsani","year":"2009","unstructured":"Ehsani MA, Shahgholi E, Rahiminejad MS, Seighali F, Rashidi A. A new index for discrimination between iron deficiency anemia and beta-thalassemia minor: results in 284 patients. Pak J Biol Sci. 2009;12:473\u20135.","journal-title":"Pak J Biol Sci"},{"key":"929_CR10","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1016\/S0140-6736(73)91878-3","volume":"1","author":"JM England","year":"1973","unstructured":"England JM, Fraser PM. Differentiation of iron deficiency from thalassaemia trait by routine blood-count. Lancet. 1973;1:449\u201352.","journal-title":"Lancet"},{"key":"929_CR11","first-page":"481","volume":"15","author":"R Green","year":"1989","unstructured":"Green R, King R. A new red cell discriminant incorporating volume dispersion for differentiating iron deficiency anemia from thalassemia minor. Blood Cells. 1989;15:481\u201391 discussion 492.","journal-title":"Blood Cells"},{"key":"929_CR12","doi-asserted-by":"publisher","first-page":"882","DOI":"10.1016\/S0140-6736(73)91446-3","volume":"1","author":"WC Mentzer","year":"1973","unstructured":"Mentzer WC. Differentiation of iron deficiency from thalassaemia trait. Lancet. 1973;1:882.","journal-title":"Lancet"},{"key":"929_CR13","doi-asserted-by":"crossref","unstructured":"Jayabose S, Giamelli J, LevondogluTugal O, Sandoval C, Ozkaynak F, Visintainer P. Differentiating iron deficiency anemia from thalassemia minor by using an RDW-based index. J Pediatr Hematol Oncol. 1999;21(4):314.","DOI":"10.1097\/00043426-199907000-00040"},{"key":"929_CR14","first-page":"409","volume":"72","author":"BM Ricerca","year":"1987","unstructured":"Ricerca BM, Storti S, d\u2019Onofrio G, Mancini S, Vittori M, Campisi S, et al. Differentiation of iron deficiency from thalassaemia trait: a new approach. Haematologica. 1987;72:409\u201313.","journal-title":"Haematologica"},{"key":"929_CR15","doi-asserted-by":"publisher","first-page":"692","DOI":"10.1016\/S0140-6736(77)92128-6","volume":"1","author":"I Shine","year":"1977","unstructured":"Shine I, Lal S. A strategy to detect beta-thalassaemia minor. Lancet. 1977;1:692\u20134.","journal-title":"Lancet"},{"key":"929_CR16","doi-asserted-by":"publisher","first-page":"324","DOI":"10.1111\/j.1751-553X.2007.00966.x","volume":"30","author":"M Sirdah","year":"2008","unstructured":"Sirdah M, Tarazi I, Al Najjar E, Al HR. Evaluation of the diagnostic reliability of different RBC indices and formulas in the differentiation of the beta-thalassaemia minor from iron deficiency in Palestinian population. Int J Lab Hematol. 2008;30:324\u201330. https:\/\/doi.org\/10.1111\/j.1751-553X.2007.00966.x .","journal-title":"Int J Lab Hematol"},{"key":"929_CR17","doi-asserted-by":"publisher","first-page":"832","DOI":"10.1016\/S0140-6736(73)90637-5","volume":"1","author":"PC Srivastava","year":"1973","unstructured":"Srivastava PC, Bevington JM. Iron deficiency and-or thalassaemia trait. Lancet. 1973;1:832.","journal-title":"Lancet"},{"key":"929_CR18","first-page":"174","volume":"45","author":"N Sirachainan","year":"2014","unstructured":"Sirachainan N, Iamsirirak P, Charoenkwan P, Kadegasem P, Wongwerawattanakoon P, Sasanakul W, et al. New mathematical formula for differentiating thalassemia trait and iron deficiency anemia in thalassemia prevalent area: a study in healthy school-age children. Southeast Asian J Trop Med Public Health. 2014;45:174\u201382.","journal-title":"Southeast Asian J Trop Med Public Health"},{"key":"929_CR19","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1093\/labmed\/lmx029","volume":"48","author":"A Hafeez Kandhro","year":"2017","unstructured":"Hafeez Kandhro A, Shoombuatong W, Prachayasittikul V, Nuchnoi P. New Bioinformatics-Based Discrimination Formulas for Differentiation of Thalassemia Traits From Iron Deficiency Anemia. Lab Med. 2017;48:230\u20137. https:\/\/doi.org\/10.1093\/labmed\/lmx029 .","journal-title":"Lab Med"},{"key":"929_CR20","doi-asserted-by":"publisher","first-page":"2451","DOI":"10.1167\/iovs.08-2594","volume":"50","author":"B van Alphen","year":"2009","unstructured":"van Alphen B, BHJ W, Frens MA. Age- and sex-related differences in contrast sensitivity in C57BL\/6 mice. Invest Ophthalmol Vis Sci. 2009;50:2451\u20138. https:\/\/doi.org\/10.1167\/iovs.08-2594 .","journal-title":"Invest Ophthalmol Vis Sci"},{"key":"929_CR21","doi-asserted-by":"publisher","first-page":"e91968","DOI":"10.1371\/journal.pone.0091968","volume":"9","author":"A Ambayya","year":"2014","unstructured":"Ambayya A, Su AT, Osman NH, Nik-Samsudin NR, Khalid K, Chang KM, et al. Haematological reference intervals in a multiethnic population. PLoS ONE. 2014;9:e91968. https:\/\/doi.org\/10.1371\/journal.pone.0091968 .","journal-title":"PLoS ONE"},{"key":"929_CR22","doi-asserted-by":"publisher","first-page":"e0114061","DOI":"10.1371\/journal.pone.0114061","volume":"10","author":"T-C Huang","year":"2015","unstructured":"Huang T-C, Wu Y-Y, Chen Y-G, Lai S-W, Wu S-C, Ye R-H, et al. Discrimination index of microcytic anemia in young soldiers: a single institutional analysis. PLoS ONE. 2015;10:e0114061. https:\/\/doi.org\/10.1371\/journal.pone.0114061 .","journal-title":"PLoS ONE"},{"key":"929_CR23","first-page":"27","volume":"8","author":"E Miri-Moghaddam","year":"2014","unstructured":"Miri-Moghaddam E, Sargolzaie N. Cut off determination of discrimination indices in differential diagnosis between iron deficiency anemia and \u03b2- thalassemia minor. Int J Hematol Oncol Stem Cell Res. 2014;8:27\u201332.","journal-title":"Int J Hematol Oncol Stem Cell Res"},{"key":"929_CR24","doi-asserted-by":"publisher","first-page":"e2015022","DOI":"10.4084\/MJHID.2015.022","volume":"7","author":"E Bordbar","year":"2015","unstructured":"Bordbar E, Taghipour M, Zucconi BE. Reliability of Different RBC Indices and Formulas in Discriminating between \u03b2-Thalassemia Minor and other Microcytic Hypochromic Cases. Mediterr J Hematol Infect Dis. 2015;7:e2015022. https:\/\/doi.org\/10.4084\/MJHID.2015.022 .","journal-title":"Mediterr J Hematol Infect Dis"},{"key":"929_CR25","doi-asserted-by":"publisher","unstructured":"Shoombuatong W, Prathipati P, Prachayasittikul V, Schaduangrat N, Malik AA, Pratiwi R, et al. Towards Predicting the Cytochrome P450 Modulation: From QSAR to proteochemometric modeling. Curr Drug Metab. 2017. https:\/\/doi.org\/10.2174\/1389200218666170320121932 .","DOI":"10.2174\/1389200218666170320121932"},{"key":"929_CR26","doi-asserted-by":"publisher","first-page":"4515","DOI":"10.2147\/DDDT.S86529","volume":"9","author":"W Shoombuatong","year":"2015","unstructured":"Shoombuatong W, Prachayasittikul V, Anuwongcharoen N, Songtawee N, Monnor T, Prachayasittikul S, et al. Navigating the chemical space of dipeptidyl peptidase-4 inhibitors. Drug Des Devel Ther. 2015;9:4515\u201349. https:\/\/doi.org\/10.2147\/DDDT.S86529 .","journal-title":"Drug Des Devel Ther"},{"key":"929_CR27","doi-asserted-by":"publisher","first-page":"452","DOI":"10.17179\/excli2015-140","volume":"14","author":"W Shoombuatong","year":"2015","unstructured":"Shoombuatong W, Prachayasittikul V, Prachayasittikul V, Nantasenamat C. Prediction of aromatase inhibitory activity using the efficient linear method (ELM). EXCLI J. 2015;14:452\u201364. https:\/\/doi.org\/10.17179\/excli2015-140 .","journal-title":"EXCLI J"},{"key":"929_CR28","doi-asserted-by":"publisher","first-page":"275","DOI":"10.4155\/fmc-2016-0188","volume":"9","author":"TS Win","year":"2017","unstructured":"Win TS, Malik AA, Prachayasittikul V, JE SW, Nantasenamat C, Shoombuatong W. HemoPred: a web server for predicting the hemolytic activity of peptides. Future Med Chem. 2017;9:275\u201391. https:\/\/doi.org\/10.4155\/fmc-2016-0188 .","journal-title":"Future Med Chem"},{"key":"929_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v025.i01","volume":"25","author":"S Le","year":"2008","unstructured":"Le S, Josse J, Husson F. FactoMineR: an R package for multivariate analysis. J Stat Software. 2008;25:1\u20138.","journal-title":"J Stat Software"},{"key":"929_CR30","volume-title":"R: A Language and Environment for Statistical Computing","author":"R Development Core Team","year":"2011","unstructured":"R Development Core Team. R: A Language and Environment for Statistical Computing. Vienna: the R Foundation for Statistical Computing; 2011. ISBN: 3\u2013900051\u201307-0. http:\/\/www.R-project.org\/"},{"key":"929_CR31","volume-title":"C4.5: Programs for Machine Learning","author":"JR Quinlan","year":"2014","unstructured":"Quinlan JR. C4.5: Programs for Machine Learning. Amsterdam: Elsevier; 2014."},{"key":"929_CR32","volume-title":"randomForest: Random Forests for Classification and Regression","author":"A Cutler","year":"2006","unstructured":"Cutler A. randomForest: Random Forests for Classification and Regression; 2006."},{"key":"929_CR33","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1109\/ANZIIS.1994.396988","volume-title":"Proceedings of ANZIIS \u201894 - Australian New Zealand intelligent information systems conference. IEEE","author":"G Holmes","year":"1994","unstructured":"Holmes G, Donkin A, Witten IH. WEKA: a machine learning workbench. In: Proceedings of ANZIIS \u201894 - Australian New Zealand intelligent information systems conference. IEEE; 1994. p. 357\u201361. https:\/\/doi.org\/10.1109\/ANZIIS.1994.396988 ."},{"key":"929_CR34","unstructured":"Breiman L, Friedman J, Stone CJ, Olshen RA. Classification and Regression Trees Taylor & Francis Group: CRC Press; 1984."},{"issue":"1","key":"929_CR35","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1093\/bib\/bbq011","volume":"12","author":"ML Calle","year":"2011","unstructured":"Calle ML, Urrea V. Letter to the editor: stability of random forest importance measures. Brief Bioinform. 2011;12(1):86\u20139.","journal-title":"Brief Bioinform"},{"key":"929_CR36","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1016\/S0731-7085(99)00272-1","volume":"22","author":"S Agatonovic-Kustrin","year":"2000","unstructured":"Agatonovic-Kustrin S, Beresford R. Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J Pharm Biomed Anal. 2000;22:717\u201327. https:\/\/doi.org\/10.1016\/S0731-7085(99)00272-1 .","journal-title":"J Pharm Biomed Anal."},{"key":"929_CR37","doi-asserted-by":"publisher","unstructured":"Liu X. Deep Recurrent Neural Network for Protein Function Prediction from Sequence. BioRxiv. 2017. https:\/\/doi.org\/10.1101\/103994 .","DOI":"10.1101\/103994"},{"key":"929_CR38","doi-asserted-by":"publisher","first-page":"3367","DOI":"10.1109\/CVPR.2015.7298958","volume-title":"2015 IEEE Conference on computer vision and pattern recognition (CVPR). IEEE","author":"M Liang","year":"2015","unstructured":"Liang M, Hu X. Recurrent convolutional neural network for object recognition. In: 2015 IEEE Conference on computer vision and pattern recognition (CVPR). IEEE; 2015. p. 3367\u201375. https:\/\/doi.org\/10.1109\/CVPR.2015.7298958 ."},{"issue":"3","key":"929_CR39","first-page":"273","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V. Support-vector networks. Mach.Learn. 1995;20(3):273\u201397.","journal-title":"Mach.Learn"},{"key":"929_CR40","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1016\/j.patcog.2016.03.028","volume":"58","author":"SM Erfani","year":"2016","unstructured":"Erfani SM, Rajasegarar S, Karunasekera S, Leckie C. High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning. Pattern Recognit. 2016;58:121\u201334. https:\/\/doi.org\/10.1016\/j.patcog.2016.03.028 .","journal-title":"Pattern Recognit"},{"key":"929_CR41","unstructured":"David Meyer, Evgenia Dimitriadou, Kurt Hornik, Andreas Weingessel and Friedrich Leisch (2017). e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien. R package version 1.6\u20138. https:\/\/CRAN.R project.org\/package=e1071"},{"key":"929_CR42","doi-asserted-by":"publisher","first-page":"546","DOI":"10.1002\/gepi.20166","volume":"30","author":"AA Motsinger","year":"2006","unstructured":"Motsinger AA, Ritchie MD. The effect of reduction in cross-validation intervals on the performance of multifactor dimensionality reduction. Genet Epidemiol. 2006;30:546\u201355. https:\/\/doi.org\/10.1002\/gepi.20166 .","journal-title":"Genet Epidemiol"},{"key":"929_CR43","unstructured":"Baratloo A, Hosseini M, Negida A, El Ashal G. Part 1: Simple definition and calculation of accuracy, sensitivity and specificity. Emerg (Tehran). Spring. 2015;3(2):48\u201349."},{"issue":"1","key":"929_CR44","doi-asserted-by":"publisher","first-page":"45","DOI":"10.4103\/0301-4738.37595","volume":"56","author":"Rajul Parikh","year":"2008","unstructured":"Parikh R, Mathai A, Parikh S, Chandra Sekhar G, Thomas R. Understanding and using sensitivity, specificity and predictive values. Indian J Ophthalmol. 2008;56(1):45\u201350.","journal-title":"Indian Journal of Ophthalmology"},{"issue":"5-6","key":"929_CR45","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1016\/j.compbiolchem.2004.09.006","volume":"28","author":"J. Gorodkin","year":"2004","unstructured":"Gorodkin J. Comparing two K-category assignments by a K-category correlation coefficient. Comput Biol Chem. 2004;28(5-6):367\u201374.","journal-title":"Computational Biology and Chemistry"},{"issue":"8","key":"929_CR46","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","volume":"27","author":"Tom Fawcett","year":"2006","unstructured":"Fawcett T. An introduction to ROC analysis. Pattern Recognit Lett. 2006;27(8):861\u201374.","journal-title":"Pattern Recognition Letters"},{"issue":"20","key":"929_CR47","doi-asserted-by":"publisher","first-page":"3940","DOI":"10.1093\/bioinformatics\/bti623","volume":"21","author":"T Sing","year":"2005","unstructured":"Sing T, et al. ROCR: visualizing classifier performance in R. Bioinformatics. 2005;21(20):3940\u20131.","journal-title":"Bioinformatics"},{"key":"929_CR48","doi-asserted-by":"publisher","first-page":"e0177678","DOI":"10.1371\/journal.pone.0177678","volume":"12","author":"S Boughorbel","year":"2017","unstructured":"Boughorbel S, Jarray F, El-Anbari M. Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric. PLoS ONE. 2017;12:e0177678. https:\/\/doi.org\/10.1371\/journal.pone.0177678 .","journal-title":"PLoS ONE"},{"key":"929_CR49","doi-asserted-by":"publisher","first-page":"5129","DOI":"10.1038\/s41598-019-41538-x","volume":"9","author":"P Agrawal","year":"2019","unstructured":"Agrawal P, Kumar S, Singh A, Raghava GPS, Singh IK. NeuroPIpred: a tool to predict, design and scan insect neuropeptides. Sci Rep. 2019;9:5129. https:\/\/doi.org\/10.1038\/s41598-019-41538-x .","journal-title":"Sci Rep"},{"key":"929_CR50","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1007\/978-1-4471-4884-5_9","volume-title":"Principles of data mining","author":"M Bramer","year":"2013","unstructured":"Bramer M. Avoiding overfitting of decision trees. In: Principles of data mining. London: Springer London; 2013. p. 121\u201336. https:\/\/doi.org\/10.1007\/978-1-4471-4884-5_9 ."},{"key":"929_CR51","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4842-3525-6","volume-title":"Applied analytics through case studies using Sas and R: implementing predictive models and machine learning techniques","author":"D Gupta","year":"2018","unstructured":"Gupta D. Applied analytics through case studies using Sas and R: implementing predictive models and machine learning techniques. Berkeley: Apress; 2018. https:\/\/doi.org\/10.1007\/978-1-4842-3525-6 ."},{"key":"929_CR52","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1007\/s00277-007-0302-x","volume":"86","author":"G Ntaios","year":"2007","unstructured":"Ntaios G, Chatzinikolaou A, Saouli Z, Girtovitis F, Tsapanidou M, Kaiafa G, et al. Discrimination indices as screening tests for beta-thalassemic trait. Ann Hematol. 2007;86:487\u201391. https:\/\/doi.org\/10.1007\/s00277-007-0302-x .","journal-title":"Ann Hematol"},{"key":"929_CR53","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1177\/147323000903700103","volume":"37","author":"V Okan","year":"2009","unstructured":"Okan V, Cigiloglu A, Cifci S, Yilmaz M, Pehlivan M. Red cell indices and functions differentiating patients with the beta-thalassaemia trait from those with iron deficiency anaemia. J Int Med Res. 2009;37:25\u201330. https:\/\/doi.org\/10.1177\/147323000903700103 .","journal-title":"J Int Med Res"},{"key":"929_CR54","unstructured":"Piplani S, Madaan M, Mannan R, Manjari M, Singh T, Lalit M. Evaluation of various discrimination indices in differentiating iron deficiency anemia and beta thalassemia trait: A practical low cost solution. Annal Pathol Lab Med. 2016;3(6):A551\u201359."},{"key":"929_CR55","doi-asserted-by":"publisher","first-page":"258","DOI":"10.3109\/03630269.2014.930044","volume":"38","author":"S Pornprasert","year":"2014","unstructured":"Pornprasert S, Panya A, Punyamung M, Yanola J, Kongpan C. Red cell indices and formulas used in differentiation of \u03b2-thalassemia trait from iron deficiency in Thai school children. Hemoglobin. 2014;38:258\u201361. https:\/\/doi.org\/10.3109\/03630269.2014.930044 .","journal-title":"Hemoglobin"},{"key":"929_CR56","doi-asserted-by":"publisher","first-page":"235","DOI":"10.3109\/03630269.2015.1048352","volume":"39","author":"S Plengsuree","year":"2015","unstructured":"Plengsuree S, Punyamung M, Yanola J, Nanta S, Jaiping K, Maneewong K, et al. Red Cell Indices and Formulas Used in Differentiation of \u03b2-Thalassemia Trait from Iron Deficiency in Thai Adults. Hemoglobin. 2015;39:235\u20139. https:\/\/doi.org\/10.3109\/03630269.2015.1048352 .","journal-title":"Hemoglobin"},{"key":"929_CR57","doi-asserted-by":"publisher","first-page":"250","DOI":"10.1159\/000337032","volume":"127","author":"K Wongprachum","year":"2012","unstructured":"Wongprachum K, Sanchaisuriya K, Sanchaisuriya P, Siridamrongvattana S, Manpeun S, Schlep FP. Proxy indicators for identifying iron deficiency among anemic vegetarians in an area prevalent for thalassemia and hemoglobinopathies. Acta Haematol. 2012;127:250\u20135. https:\/\/doi.org\/10.1159\/000337032 .","journal-title":"Acta Haematol"},{"key":"929_CR58","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1097\/00007632-199903010-00021","volume":"24","author":"JD Lurie","year":"1999","unstructured":"Lurie JD, Sox HC. Principles of medical decision making. Spine. 1999;24:493\u20138.","journal-title":"Spine"},{"key":"929_CR59","first-page":"787","volume":"110","author":"AF Krieg","year":"1986","unstructured":"Krieg AF, Abendroth TW, Bongiovanni MB. When is a diagnostic test result positive? Decision tree models based on net utility and threshold. Arch Pathol Lab Med. 1986;110:787\u201391.","journal-title":"Arch Pathol Lab Med"},{"key":"929_CR60","doi-asserted-by":"publisher","first-page":"293216","DOI":"10.1155\/2014\/293216","volume":"2014","author":"S Verma","year":"2014","unstructured":"Verma S, Gupta R, Kudesia M, Mathur A, Krishan G, Singh S. Coexisting iron deficiency anemia and Beta thalassemia trait: effect of iron therapy on red cell parameters and hemoglobin subtypes. ISRN Hematol. 2014;2014:293216. https:\/\/doi.org\/10.1155\/2014\/293216 .","journal-title":"ISRN Hematol"},{"key":"929_CR61","doi-asserted-by":"publisher","first-page":"44","DOI":"10.3329\/jom.v19i1.34842","volume":"19","author":"N Mohammad","year":"2017","unstructured":"Mohammad N, Chowdhury MJ, Alam MR, Kabir AL, Rahaman MFU, Chakrabarty B. Co-existence of iron deficiency in beta thalassaemia trait. J Med. 2017;19:44. https:\/\/doi.org\/10.3329\/jom.v19i1.34842 .","journal-title":"J Med"},{"key":"929_CR62","doi-asserted-by":"publisher","first-page":"1565","DOI":"10.1038\/nbt1206-1565","volume":"24","author":"WS Noble","year":"2006","unstructured":"Noble WS. What is a support vector machine? Nat Biotechnol. 2006;24:1565\u20137. https:\/\/doi.org\/10.1038\/nbt1206-1565 .","journal-title":"Nat Biotechnol"},{"key":"929_CR63","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1021\/ci0342472","volume":"44","author":"DM Hawkins","year":"2004","unstructured":"Hawkins DM. The problem of overfitting. J Chem Inf Comput Sci. 2004;44:1\u201312. https:\/\/doi.org\/10.1021\/ci0342472 .","journal-title":"J Chem Inf Comput Sci"},{"key":"929_CR64","doi-asserted-by":"publisher","first-page":"1947","DOI":"10.1021\/ci034160g","volume":"43","author":"V Svetnik","year":"2003","unstructured":"Svetnik V, Liaw A, Tong C, Culberson JC, Sheridan RP, Feuston BP. Random forest: a classification and regression tool for compound classification and QSAR modeling. J Chem Inf Comput Sci. 2003;43:1947\u201358. https:\/\/doi.org\/10.1021\/ci034160g .","journal-title":"J Chem Inf Comput Sci"},{"key":"929_CR65","doi-asserted-by":"publisher","first-page":"e0155290","DOI":"10.1371\/journal.pone.0155290","volume":"11","author":"YH Li","year":"2016","unstructured":"Li YH, Xu JY, Tao L, Li XF, Li S, Zeng X, et al. SVM-Prot 2016: A Web-Server for Machine Learning Prediction of Protein Functional Families from Sequence Irrespective of Similarity. PLoS ONE. 2016;11:e0155290. https:\/\/doi.org\/10.1371\/journal.pone.0155290 .","journal-title":"PLoS ONE"},{"key":"929_CR66","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1186\/1687-4153-2013-10","volume":"2013","author":"ER Dougherty","year":"2013","unstructured":"Dougherty ER, Dalton LA. Scientific knowledge is possible with small-sample classification. EURASIP J Bioinform Syst Biol. 2013;2013:10. https:\/\/doi.org\/10.1186\/1687-4153-2013-10 .","journal-title":"EURASIP J Bioinform Syst Biol"},{"key":"929_CR67","doi-asserted-by":"publisher","first-page":"R5","DOI":"10.1186\/bcr2468","volume":"12","author":"V Popovici","year":"2010","unstructured":"Popovici V, Chen W, Gallas BG, Hatzis C, Shi W, Samuelson FW, et al. Effect of training-sample size and classification difficulty on the accuracy of genomic predictors. Breast Cancer Res. 2010;12:R5. https:\/\/doi.org\/10.1186\/bcr2468 .","journal-title":"Breast Cancer Res"},{"key":"929_CR68","doi-asserted-by":"publisher","unstructured":"Khan SH, Hayat M, Bennamoun M, Sohel FA, Togneri R. Cost-Sensitive Learning of Deep Feature Representations From Imbalanced Data. IEEE Trans Neural Netw Learn Syst. 2017. https:\/\/doi.org\/10.1109\/TNNLS.2017.2732482 .","DOI":"10.1109\/TNNLS.2017.2732482"},{"key":"929_CR69","doi-asserted-by":"publisher","first-page":"5423204","DOI":"10.1155\/2016\/5423204","volume":"2016","author":"H Guo","year":"2016","unstructured":"Guo H, Zhi W, Liu H, Xu M. Imbalanced learning based on logistic discrimination. Comput Intell Neurosci. 2016;2016:5423204. https:\/\/doi.org\/10.1155\/2016\/5423204 .","journal-title":"Comput Intell Neurosci"}],"updated-by":[{"DOI":"10.1186\/s12911-019-0977-7","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2019,11,19]],"date-time":"2019-11-19T00:00:00Z","timestamp":1574121600000}}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-019-0929-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s12911-019-0929-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-019-0929-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,3]],"date-time":"2022-10-03T21:58:37Z","timestamp":1664834317000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-019-0929-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,7]]},"references-count":69,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2019,12]]}},"alternative-id":["929"],"URL":"https:\/\/doi.org\/10.1186\/s12911-019-0929-2","relation":{"correction":[{"id-type":"doi","id":"10.1186\/s12911-019-0977-7","asserted-by":"object"}]},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11,7]]},"assertion":[{"value":"11 October 2017","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 October 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 November 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 November 2019","order":4,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":5,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Following publication of the original article [1], the authors reported an error in one of the authors\u2019 names. In this Correction the incorrect and correct author name are shown. The original publication of this article has been corrected.","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The study was conducted under the approval of Mahidol University Central Institutional Review Board (MU-CIRB: 2016\/084.0311) We received a participant consent waiver from MU-CIRB. All information of subjects was de-identified prior data analysis.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not Applicable","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interest and the research was conducted in the absence of any commercial or financial relationship that could be influenced as potential conflict of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"212"}}