{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T17:06:29Z","timestamp":1767891989261,"version":"3.49.0"},"reference-count":81,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,9,2]],"date-time":"2023-09-02T00:00:00Z","timestamp":1693612800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,9,2]],"date-time":"2023-09-02T00:00:00Z","timestamp":1693612800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"EU Interreg 5a DE-DK project Access & Acceleration"},{"name":"University Library of Southern Denmark"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Alcohol use disorder (AUD) causes significant morbidity, mortality, and injuries. According to reports, approximately 5% of all registered deaths in Denmark could be due to AUD. The problem is compounded by the late identification of patients with AUD, a situation that can cause enormous problems, from psychological to physical to economic problems. Many individuals suffering from AUD never undergo specialist treatment during their addiction due to obstacles such as taboo and the poor performance of current screening tools. Therefore, there is a lack of rapid intervention. This can be mitigated by the early detection of patients with AUD. A clinical decision support system (DSS) powered by machine learning (ML) methods can be used to diagnose patients\u2019 AUD status earlier.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>This study proposes an effective AUD prediction model (AUDPM), which can be used in a DSS. The proposed model consists of four distinct components: (1) imputation to address missing values using the k-nearest neighbours approach, (2) recursive feature elimination with cross validation to select the most relevant subset of features, (3) a hybrid synthetic minority oversampling technique-edited nearest neighbour approach to remove noise and balance the distribution of the training data, and (4) an ML model for the early detection of patients with AUD.<\/jats:p>\n                <jats:p>Two data sources, including a questionnaire and electronic health records of 2571 patients, were collected from Odense University Hospital in the Region of Southern Denmark for the AUD-Dataset. Then, the AUD-Dataset was used to build ML models. The results of different ML models, such as support vector machine, K-nearest neighbour, decision tree, random forest, and extreme gradient boosting, were compared. Finally, a combination of all these models in an ensemble learning approach was selected for the AUDPM.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The results revealed that the proposed ensemble AUDPM outperformed other single models and our previous study results, achieving 0.96, 0.94, 0.95, and 0.97 precision, recall, F1-score, and accuracy, respectively. In addition, we designed and developed an AUD-DSS prototype.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>It was shown that our proposed AUDPM achieved high classification performance. In addition, we identified clinical factors related to the early detection of patients with AUD. The designed AUD-DSS is intended to be integrated into the existing Danish health care system to provide novel information to clinical staff if a patient shows signs of harmful alcohol use; in other words, it gives staff a good reason for having a conversation with patients for whom a conversation is relevant.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-023-05450-6","type":"journal-article","created":{"date-parts":[[2023,9,2]],"date-time":"2023-09-02T02:01:53Z","timestamp":1693620113000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["AUD-DSS: a decision support system for early detection of patients with alcohol use disorder"],"prefix":"10.1186","volume":"24","author":[{"given":"Ali","family":"Ebrahimi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Uffe Kock","family":"Wiil","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruben","family":"Baskaran","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdolrahman","family":"Peimankar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kjeld","family":"Andersen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anette S\u00f8gaard","family":"Nielsen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,2]]},"reference":[{"key":"5450_CR1","unstructured":"A. P. Association. Diagnostic and statistical manual of mental disorders (DSM-5\u00ae). American Psychiatric Pub, 2013."},{"issue":"2","key":"5450_CR2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11920-019-0997-0","volume":"21","author":"J Rehm","year":"2019","unstructured":"Rehm J, Shield KD. Global burden of disease and the impact of mental and addictive disorders. Curr Psychiatry Rep. 2019;21(2):1\u20137.","journal-title":"Curr Psychiatry Rep"},{"key":"5450_CR3","doi-asserted-by":"crossref","first-page":"SART.S23746","DOI":"10.4137\/SART.S23746","volume":"9","author":"M Ramstedt","year":"2015","unstructured":"Ramstedt M, et al. Harm experienced from the heavy drinking of family and friends in the general population: a comparative study of six Northern European countries. Subst Abuse Res Treat. 2015;9:SART.S23746.","journal-title":"Subst Abuse Res Treat"},{"key":"5450_CR4","doi-asserted-by":"crossref","first-page":"SART.S23504","DOI":"10.4137\/SART.S23504","volume":"9","author":"IS Moan","year":"2015","unstructured":"Moan IS, et al. Experienced harm from other people\u2019s drinking: a comparison of Northern European countries. Subst Abuse Res Treat. 2015;9:SART.S23504.","journal-title":"Subst Abuse Res Treat"},{"issue":"18","key":"5450_CR5","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.1001\/jama.1993.03510180077038","volume":"270","author":"JM McGinnis","year":"1993","unstructured":"McGinnis JM, Foege WH. Actual causes of death in the United States. JAMA. 1993;270(18):2207\u201312.","journal-title":"JAMA"},{"issue":"10","key":"5450_CR6","first-page":"213","volume":"63","author":"K Gonzales","year":"2014","unstructured":"Gonzales K, et al. Alcohol-attributable deaths and years of potential life lost\u201411 states, 2006\u20132010. MMWR Morb Mortal Wkly Rep. 2014;63(10):213.","journal-title":"MMWR Morb Mortal Wkly Rep"},{"issue":"4","key":"5450_CR7","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1111\/acps.12330","volume":"131","author":"J Westman","year":"2015","unstructured":"Westman J, et al. Mortality and life expectancy of people with alcohol use disorder in Denmark, Finland and Sweden. Acta Psychiatr Scand. 2015;131(4):297\u2013306.","journal-title":"Acta Psychiatr Scand"},{"issue":"2","key":"5450_CR8","first-page":"128","volume":"39","author":"AB Gottlieb Hansen","year":"2011","unstructured":"Gottlieb Hansen AB, Hvidtfeldt UA, Gr\u00f8nb\u00e6k M, Becker U, S\u00f8gaard Nielsen A, Schurmann Tolstrup J. The number of persons with alcohol problems in the Danish population. Scand J Soc Med. 2011;39(2):128\u201336.","journal-title":"Scand J Soc Med"},{"key":"5450_CR9","unstructured":"W. H. Organization, Regional Office for Europe. European health for all database (HFA-DB)[Internet] 2013 [Citado 10 Jun 2013]."},{"issue":"8","key":"5450_CR10","doi-asserted-by":"crossref","first-page":"1086","DOI":"10.1111\/j.1360-0443.2006.01491.x","volume":"101","author":"J Rehm","year":"2006","unstructured":"Rehm J, Taylor B, Patra J. Volume of alcohol consumption, patterns of drinking and burden of disease in the European region 2002. Addiction. 2006;101(8):1086\u201395.","journal-title":"Addiction"},{"issue":"1","key":"5450_CR11","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1370\/afm.1742","volume":"13","author":"J Rehm","year":"2015","unstructured":"Rehm J, et al. General practitioners recognizing alcohol dependence: a large cross-sectional study in 6 European countries. Ann Fam Med. 2015;13(1):28\u201332.","journal-title":"Ann Fam Med"},{"issue":"1","key":"5450_CR12","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1007\/s10389-017-0841-0","volume":"26","author":"A-S Schwarz","year":"2018","unstructured":"Schwarz A-S, Nielsen B, Nielsen AS. Changes in profile of patients seeking alcohol treatment and treatment outcomes following policy changes. J Public Health. 2018;26(1):59\u201367.","journal-title":"J Public Health"},{"issue":"8","key":"5450_CR13","first-page":"1111","volume":"161","author":"A Nielsen","year":"1999","unstructured":"Nielsen A, Nielsen B, Benjaminsen S, Petersen P, Rask P, Gansmo A. Differences between male and female alcoholics and differences in their need of treatment. Ugeskr Laeger. 1999;161(8):1111\u20136.","journal-title":"Ugeskr Laeger"},{"issue":"9662","key":"5450_CR14","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1016\/S0140-6736(09)60009-X","volume":"373","author":"MA Schuckit","year":"2009","unstructured":"Schuckit MA. Alcohol-use disorders. Lancet. 2009;373(9662):492\u2013501.","journal-title":"Lancet"},{"key":"5450_CR15","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1515\/nsad-2016-0034","volume":"33","author":"R Hellum","year":"2016","unstructured":"Hellum R, Bjerregaard L, Nielsen AS. Factors influencing whether nurses talk to somatic patients about their alcohol consumption. Nordic Stud Alcohol Drugs. 2016;33:415\u201336.","journal-title":"Nordic Stud Alcohol Drugs"},{"key":"5450_CR16","doi-asserted-by":"publisher","DOI":"10.1080\/07347324.2020.1803168","author":"C Oxholm","year":"2020","unstructured":"Oxholm C, Christensen A-MS, Christiansen R, Nielsen AS. Can we talk about alcohol for a minute? Thoughts and opinions expressed by health professionals and patients at a somatic hospital. Alcohol Treat Q. 2020. https:\/\/doi.org\/10.1080\/07347324.2020.1803168.","journal-title":"Alcohol Treat Q"},{"issue":"Suppl 2","key":"5450_CR17","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1111\/add.13652","volume":"112","author":"J Vendetti","year":"2017","unstructured":"Vendetti J, Gmyrek A, Damon D, Singh M, McRee B, Del Boca F. Screening, brief intervention and referral to treatment (SBIRT): implementation barriers, facilitators and model migration. Addiction. 2017;112(Suppl 2):23\u201333. https:\/\/doi.org\/10.1111\/add.13652.","journal-title":"Addiction"},{"issue":"10200","key":"5450_CR18","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1016\/S0140-6736(19)31775-1","volume":"394","author":"AF Carvalho","year":"2019","unstructured":"Carvalho AF, Heilig M, Perez A, Probst C, Rehm J. Alcohol use disorders. Lancet. 2019;394(10200):781\u201392.","journal-title":"Lancet"},{"key":"5450_CR19","doi-asserted-by":"publisher","first-page":"CD004148","DOI":"10.1002\/14651858.CD004148.pub4","volume":"2","author":"EF Kaner","year":"2018","unstructured":"Kaner EF, et al. Effectiveness of brief alcohol interventions in primary care populations. Cochrane Database Syst Rev. 2018;2:CD004148. https:\/\/doi.org\/10.1002\/14651858.CD004148.pub4.","journal-title":"Cochrane Database Syst Rev"},{"issue":"2","key":"5450_CR20","doi-asserted-by":"crossref","first-page":"679","DOI":"10.3390\/s23020679","volume":"23","author":"A Peimankar","year":"2023","unstructured":"Peimankar A, Winther TS, Ebrahimi A, Wiil UK. A machine learning approach for walking classification in elderly people with gait disorders. Sensors. 2023;23(2):679.","journal-title":"Sensors"},{"key":"5450_CR21","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-15-4112-4_9","author":"M Sucharitha","year":"2021","unstructured":"Sucharitha M, Chakraborty C, Srinivasa Rao S, Reddy V. Early detection of dementia disease using data mining techniques. Internet Things Healthc Technol. 2021. https:\/\/doi.org\/10.1007\/978-981-15-4112-4_9.","journal-title":"Internet Things Healthc Technol"},{"key":"5450_CR22","doi-asserted-by":"publisher","DOI":"10.1007\/s13198-021-01174-z","author":"A Kishor","year":"2021","unstructured":"Kishor A, Chakraborty C. Early and accurate prediction of diabetics based on FCBF feature selection and SMOTE. Int J Syst Assur Eng Manag. 2021. https:\/\/doi.org\/10.1007\/s13198-021-01174-z.","journal-title":"Int J Syst Assur Eng Manag"},{"key":"5450_CR23","doi-asserted-by":"crossref","first-page":"104790","DOI":"10.1016\/j.ijmedinf.2022.104790","volume":"163","author":"MS Jahan","year":"2022","unstructured":"Jahan MS, Mansourvar M, Puthusserypady S, Wiil UK, Peimankar A. Short-term atrial fibrillation detection using electrocardiograms: a comparison of machine learning approaches. Int J Med Inform. 2022;163:104790.","journal-title":"Int J Med Inform"},{"issue":"4","key":"5450_CR24","doi-asserted-by":"crossref","first-page":"662","DOI":"10.1111\/add.14504","volume":"114","author":"MH Afzali","year":"2019","unstructured":"Afzali MH, et al. Machine-learning prediction of adolescent alcohol use: a cross-study, cross-cultural validation. Addiction. 2019;114(4):662\u201371.","journal-title":"Addiction"},{"issue":"3","key":"5450_CR25","doi-asserted-by":"publisher","first-page":"397","DOI":"10.3122\/jabfm.2020.03.190421","volume":"33","author":"LN Bonnell","year":"2020","unstructured":"Bonnell LN, Littenberg B, Wshah SR, Rose GL. A machine learning approach to identification of unhealthy drinking. J Am Board Fam Med. 2020;33(3):397\u2013406. https:\/\/doi.org\/10.3122\/jabfm.2020.03.190421.","journal-title":"J Am Board Fam Med"},{"issue":"10","key":"5450_CR26","doi-asserted-by":"publisher","first-page":"e0241268","DOI":"10.1371\/journal.pone.0241268","volume":"15","author":"F Chen","year":"2020","unstructured":"Chen F, et al. Discrimination of alcohol dependence based on the convolutional neural network. PLoS One. 2020;15(10):e0241268. https:\/\/doi.org\/10.1371\/journal.pone.0241268.","journal-title":"PLoS One"},{"key":"5450_CR27","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.drugalcdep.2019.01.016","volume":"197","author":"DA Ngo","year":"2019","unstructured":"Ngo DA, Rege SV, Ait-Daoud N, Holstege CP. Development and validation of a risk predictive model for student harmful drinking\u2014a longitudinal data linkage study. Drug Alcohol Depend. 2019;197:102\u20137. https:\/\/doi.org\/10.1016\/j.drugalcdep.2019.01.016.","journal-title":"Drug Alcohol Depend"},{"key":"5450_CR28","doi-asserted-by":"publisher","unstructured":"Sisodia DS, Agrawal R, Sisodia D. A comparative performance of classification algorithms in predicting alcohol consumption among secondary school students. In: International conference on Machine Intelligence and Signal Processing, in Advances in Intelligent Systems and Computing MISP 2017, Indore, vol. 748. Springer Verlag; 2019, pp. 523\u2013532, Doi: https:\/\/doi.org\/10.1007\/978-981-13-0923-6_45. https:\/\/link.springer.com\/content\/pdf\/10.1007%2F978-981-13-0923-6_45.pdf","DOI":"10.1007\/978-981-13-0923-6_45"},{"issue":"5","key":"5450_CR29","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1016\/j.bpsc.2020.01.011","volume":"5","author":"S Silveira","year":"2020","unstructured":"Silveira S, et al. Impact of childhood trauma on executive function in adolescence-mediating functional brain networks and prediction of high-risk drinking. Biol Psychiatry-Cognit Neurosci Neuroimaging. 2020;5(5):499\u2013509. https:\/\/doi.org\/10.1016\/j.bpsc.2020.01.011.","journal-title":"Biol Psychiatry-Cognit Neurosci Neuroimaging"},{"key":"5450_CR30","doi-asserted-by":"crossref","first-page":"1133","DOI":"10.1038\/s41380-019-0534-x","volume":"26","author":"S Kinreich","year":"2019","unstructured":"Kinreich S, et al. Predicting risk for alcohol use disorder using longitudinal data with multimodal biomarkers and family history: a machine learning study. Mol Psychiatry. 2019;26:1133\u201341.","journal-title":"Mol Psychiatry"},{"key":"5450_CR31","doi-asserted-by":"publisher","DOI":"10.1038\/s41380-020-0771-z","author":"JL Gowin","year":"2021","unstructured":"Gowin JL, Manza P, Ramchandani VA, Volkow ND. Neuropsychosocial markers of binge drinking in young adults. Mol Psychiatry. 2021. https:\/\/doi.org\/10.1038\/s41380-020-0771-z.","journal-title":"Mol Psychiatry"},{"key":"5450_CR32","doi-asserted-by":"publisher","unstructured":"Ebrahimi A, Wiil UK, Andersen K, Mansourvar M, Nielsen AS. A predictive machine learning model to determine alcohol use disorder. In: 2020 IEEE Symposium on Computers and Communications (ISCC); 2020, pp. 1\u20137, doi: https:\/\/doi.org\/10.1109\/ISCC50000.2020.9219685. Available: https:\/\/ieeexplore.ieee.org\/document\/9219685\/","DOI":"10.1109\/ISCC50000.2020.9219685"},{"key":"5450_CR33","doi-asserted-by":"crossref","first-page":"151697","DOI":"10.1109\/ACCESS.2021.3126777","volume":"9","author":"A Ebrahimi","year":"2021","unstructured":"Ebrahimi A, et al. Predicting the risk of alcohol use disorder using machine learning: a systematic literature review. IEEE Access. 2021;9:151697\u2013712.","journal-title":"IEEE Access"},{"issue":"1","key":"5450_CR34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12911-022-02051-w","volume":"22","author":"A Ebrahimi","year":"2022","unstructured":"Ebrahimi A, Wiil UK, Naemi A, Mansourvar M, Andersen K, Nielsen AS. Identification of clinical factors related to prediction of alcohol use disorder from electronic health records using feature selection methods. BMC Med Inform Decis Mak. 2022;22(1):1\u201325.","journal-title":"BMC Med Inform Decis Mak"},{"key":"5450_CR35","doi-asserted-by":"crossref","first-page":"133034","DOI":"10.1109\/ACCESS.2020.3010511","volume":"8","author":"NL Fitriyani","year":"2020","unstructured":"Fitriyani NL, Syafrudin M, Alfian G, Rhee J. HDPM: an effective heart disease prediction model for a clinical decision support system. IEEE Access. 2020;8:133034\u201350.","journal-title":"IEEE Access"},{"issue":"11","key":"5450_CR36","doi-asserted-by":"crossref","first-page":"e052663","DOI":"10.1136\/bmjopen-2021-052663","volume":"11","author":"A Naemi","year":"2021","unstructured":"Naemi A, Schmidt T, Mansourvar M, Naghavi-Behzad M, Ebrahimi A, Wiil UK. Machine learning techniques for mortality prediction in emergency departments: a systematic review. BMJ Open. 2021;11(11):e052663.","journal-title":"BMJ Open"},{"key":"5450_CR37","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.drugalcdep.2018.12.017","volume":"196","author":"A-S Schwarz","year":"2019","unstructured":"Schwarz A-S, Nielsen B, S\u00f8gaard J, Nielsen AS. Making a bridge between general hospital and specialised community-based treatment for alcohol use disorder\u2014a pragmatic randomised controlled trial. Drug Alcohol Depend. 2019;196:51\u20136.","journal-title":"Drug Alcohol Depend"},{"key":"5450_CR38","doi-asserted-by":"publisher","DOI":"10.1007\/s10389-017-0885-1","author":"A-S Schwarz","year":"2018","unstructured":"Schwarz A-S, Nielsen B, Nielsen AS. Lifestyle factors in somatic patients with and without potential alcohol problems. J Public Health. 2018. https:\/\/doi.org\/10.1007\/s10389-017-0885-1.","journal-title":"J Public Health"},{"issue":"1","key":"5450_CR39","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1186\/s12913-016-1376-8","volume":"16","author":"A-S Schwarz","year":"2016","unstructured":"Schwarz A-S, Bilberg R, Bjerregaard L, Nielsen B, S\u00f8gaard J, Nielsen AS. Relay model for recruiting alcohol dependent patients in general hospitals-a single-blind pragmatic randomized trial. BMC Health Serv Res. 2016;16(1):132.","journal-title":"BMC Health Serv Res"},{"issue":"1","key":"5450_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12916-014-0241-z","volume":"13","author":"GS Collins","year":"2015","unstructured":"Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement. BMC Med. 2015;13(1):1. https:\/\/doi.org\/10.1186\/s12916-014-0241-z.","journal-title":"BMC Med"},{"issue":"6","key":"5450_CR41","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1111\/j.1360-0443.1993.tb02093.x","volume":"88","author":"JB Saunders","year":"1993","unstructured":"Saunders JB, Aasland OG, Babor TF, De la Fuente JR, Grant M. Development of the alcohol use disorders identification test (AUDIT): WHO collaborative project on early detection of persons with harmful alcohol consumption-II. Addiction. 1993;88(6):791\u2013804.","journal-title":"Addiction"},{"key":"5450_CR42","first-page":"1","volume":"2","author":"TF Babor","year":"2001","unstructured":"Babor TF, Higgins-Biddle JC, Saunders JB, Monteiro MG. The alcohol use disorders identification test. Guidel Use Prim Care. 2001;2:1\u201341.","journal-title":"Guidel Use Prim Care"},{"key":"5450_CR43","unstructured":"Babor TF, de la Fuente JR, Saunders J, Grant M. AUDIT: the alcohol use disorders identification test: guidelines for use in primary health care. In: AUDIT: The alcohol use disorders identification test: Guidelines for use in primary health care: World Health Organization; 1992."},{"key":"5450_CR44","doi-asserted-by":"crossref","unstructured":"De Silva H, Perera AS. Missing data imputation using Evolutionary k-Nearest neighbor algorithm for gene expression data. In: 2016 sixteenth international conference on advances in ICT for emerging regions (ICTer): IEEE; 2016, pp. 141\u2013146.","DOI":"10.1109\/ICTER.2016.7829911"},{"key":"5450_CR45","unstructured":"W. H. Organization, International statistical classification of diseases and related health problems: alphabetical index. World Health Organization; 2004."},{"issue":"1\u20133","key":"5450_CR46","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1023\/A:1012487302797","volume":"46","author":"I Guyon","year":"2002","unstructured":"Guyon I, Weston J, Barnhill S, Vapnik V. Gene selection for cancer classification using support vector machines. Mach Learn. 2002;46(1\u20133):389\u2013422.","journal-title":"Mach Learn"},{"issue":"6","key":"5450_CR47","doi-asserted-by":"crossref","first-page":"301","DOI":"10.3390\/genes9060301","volume":"9","author":"Q Chen","year":"2018","unstructured":"Chen Q, Meng Z, Liu X, Jin Q, Su R. Decision variants for the automatic determination of optimal feature subset in RF-RFE. Genes. 2018;9(6):301.","journal-title":"Genes"},{"key":"5450_CR48","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.patrec.2020.03.004","volume":"133","author":"R Zhu","year":"2020","unstructured":"Zhu R, Guo Y, Xue J-H. Adjusting the imbalance ratio by the dimensionality of imbalanced data. Pattern Recogn Lett. 2020;133:217\u201323.","journal-title":"Pattern Recogn Lett"},{"key":"5450_CR49","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.neucom.2017.03.011","volume":"243","author":"N Ofek","year":"2017","unstructured":"Ofek N, Rokach L, Stern R, Shabtai A. Fast-CBUS: a fast clustering-based undersampling method for addressing the class imbalance problem. Neurocomputing. 2017;243:88\u2013102.","journal-title":"Neurocomputing"},{"issue":"11","key":"5450_CR50","doi-asserted-by":"crossref","first-page":"2402","DOI":"10.1109\/TCYB.2014.2372060","volume":"45","author":"WW Ng","year":"2014","unstructured":"Ng WW, Hu J, Yeung DS, Yin S, Roli F. Diversified sensitivity-based undersampling for imbalance classification problems. IEEE Trans Cybern. 2014;45(11):2402\u201312.","journal-title":"IEEE Trans Cybern"},{"key":"5450_CR51","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.eswa.2016.12.035","volume":"73","author":"G Haixiang","year":"2017","unstructured":"Haixiang G, Yijing L, Shang J, Mingyun G, Yuanyue H, Bing G. Learning from class-imbalanced data: review of methods and applications. Expert Syst Appl. 2017;73:220\u201339.","journal-title":"Expert Syst Appl"},{"key":"5450_CR52","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: synthetic minority over-sampling technique. J Artif Intell Res. 2002;16:321\u201357.","journal-title":"J Artif Intell Res"},{"issue":"1","key":"5450_CR53","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1145\/1007730.1007735","volume":"6","author":"GE Batista","year":"2004","unstructured":"Batista GE, Prati RC, Monard MC. A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD Explor Newsl. 2004;6(1):20\u20139.","journal-title":"ACM SIGKDD Explor Newsl"},{"key":"5450_CR54","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1109\/TSMC.1972.4309137","volume":"3","author":"DL Wilson","year":"1972","unstructured":"Wilson DL. Asymptotic properties of nearest neighbor rules using edited data. IEEE Trans Syst Man Cybern. 1972;3:408\u201321.","journal-title":"IEEE Trans Syst Man Cybern"},{"issue":"2","key":"5450_CR55","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/S0893-6080(05)80023-1","volume":"5","author":"DH Wolpert","year":"1992","unstructured":"Wolpert DH. Stacked generalization. Neural Netw. 1992;5(2):241\u201359.","journal-title":"Neural Netw"},{"key":"5450_CR56","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511801389","volume-title":"An introduction to support vector machines and other kernel-based learning methods","author":"N Cristianini","year":"2000","unstructured":"Cristianini N, Shawe-Taylor J. An introduction to support vector machines and other kernel-based learning methods. Cambridge: Cambridge University Press; 2000."},{"key":"5450_CR57","doi-asserted-by":"crossref","unstructured":"Mucherino A, Papajorgji PJ, Pardalos PM. K-nearest neighbor classification. In: Data mining in agriculture. Springer; 2009, pp. 83\u2013106.","DOI":"10.1007\/978-0-387-88615-2_4"},{"issue":"1","key":"5450_CR58","first-page":"81","volume":"1","author":"JR Quinlan","year":"1986","unstructured":"Quinlan JR. Induction of decision trees. Mach Learn. 1986;1(1):81\u2013106.","journal-title":"Mach Learn"},{"issue":"9","key":"5450_CR59","doi-asserted-by":"crossref","first-page":"1426","DOI":"10.1017\/S0033291719000151","volume":"49","author":"AB Shatte","year":"2019","unstructured":"Shatte AB, Hutchinson DM, Teague SJ. Machine learning in mental health: a scoping review of methods and applications. Psychol Med. 2019;49(9):1426\u201348.","journal-title":"Psychol Med"},{"issue":"3","key":"5450_CR60","doi-asserted-by":"crossref","first-page":"1937","DOI":"10.1007\/s10462-020-09896-5","volume":"54","author":"C Bent\u00e9jac","year":"2021","unstructured":"Bent\u00e9jac C, Cs\u00f6rg\u0151 A, Mart\u00ednez-Mu\u00f1oz G. A comparative analysis of gradient boosting algorithms. Artif Intell Rev. 2021;54(3):1937\u201367.","journal-title":"Artif Intell Rev"},{"issue":"5","key":"5450_CR61","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1001\/jama.2016.7653","volume":"316","author":"J Tolles","year":"2016","unstructured":"Tolles J, Meurer WJ. Logistic regression: relating patient characteristics to outcomes. JAMA. 2016;316(5):533\u20134.","journal-title":"JAMA"},{"issue":"2","key":"5450_CR62","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1080\/13658816.2020.1808897","volume":"35","author":"Z Fang","year":"2021","unstructured":"Fang Z, Wang Y, Peng L, Hong H. A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping. Int J Geogr Inf Sci. 2021;35(2):321\u201347.","journal-title":"Int J Geogr Inf Sci"},{"issue":"1","key":"5450_CR63","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12911-022-01775-z","volume":"22","author":"S Sadeghi","year":"2022","unstructured":"Sadeghi S, Khalili D, Ramezankhani A, Mansournia MA, Parsaeian M. Diabetes mellitus risk prediction in the presence of class imbalance using flexible machine learning methods. BMC Med Inform Decis Mak. 2022;22(1):1\u201312.","journal-title":"BMC Med Inform Decis Mak"},{"key":"5450_CR64","doi-asserted-by":"crossref","unstructured":"Su W, Yuan Y, Zhu M. A relationship between the average precision and the area under the ROC curve. In: Proceedings of the 2015 international conference on the theory of information retrieval; 2015, pp. 349\u2013352.","DOI":"10.1145\/2808194.2809481"},{"issue":"1","key":"5450_CR65","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1016\/j.eswa.2009.05.045","volume":"37","author":"MMR Krishnan","year":"2010","unstructured":"Krishnan MMR, Banerjee S, Chakraborty C, Chakraborty C, Ray AK. Statistical analysis of mammographic features and its classification using support vector machine. Expert Syst Appl. 2010;37(1):470\u20138.","journal-title":"Expert Syst Appl"},{"issue":"4","key":"5450_CR66","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1093\/clinchem\/39.4.561","volume":"39","author":"MH Zweig","year":"1993","unstructured":"Zweig MH, Campbell G. Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem. 1993;39(4):561\u201377.","journal-title":"Clin Chem"},{"key":"5450_CR67","doi-asserted-by":"crossref","unstructured":"Sameer M, Gupta AK, Chakraborty C, Gupta B. ROC analysis for detection of epileptical seizures using haralick features of gamma band. In: 2020 National conference on communications (NCC): IEEE; 2020, pp. 1\u20135.","DOI":"10.1109\/NCC48643.2020.9056027"},{"issue":"4","key":"5450_CR68","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1007\/s13312-011-0055-4","volume":"48","author":"R Kumar","year":"2011","unstructured":"Kumar R, Indrayan A. Receiver operating characteristic (ROC) curve for medical researchers. Indian Pediatr. 2011;48(4):277\u201387.","journal-title":"Indian Pediatr"},{"issue":"2","key":"5450_CR69","first-page":"111","volume":"4","author":"S Safari","year":"2016","unstructured":"Safari S, Baratloo A, Elfil M, Negida A. Evidence based emergency medicine; part 5 receiver operating curve and area under the curve. Emergency. 2016;4(2):111.","journal-title":"Emergency"},{"issue":"11","key":"5450_CR70","doi-asserted-by":"crossref","first-page":"5364","DOI":"10.1109\/JBHI.2022.3197910","volume":"26","author":"TK Dash","year":"2022","unstructured":"Dash TK, Chakraborty C, Mahapatra S, Panda G. Gradient boosting machine and efficient combination of features for speech-based detection of COVID-19. IEEE J Biomed Health Inform. 2022;26(11):5364\u201371.","journal-title":"IEEE J Biomed Health Inform"},{"issue":"1","key":"5450_CR71","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s41512-020-00090-3","volume":"5","author":"QM Zhou","year":"2021","unstructured":"Zhou QM, Zhe L, Brooke RJ, Hudson MM, Yuan Y. A relationship between the incremental values of area under the ROC curve and of area under the precision-recall curve. Diagn Progn Res. 2021;5(1):1\u201315.","journal-title":"Diagn Progn Res"},{"issue":"1","key":"5450_CR72","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L. Random forests. Mach Learn. 2001;45(1):5\u201332.","journal-title":"Mach Learn"},{"key":"5450_CR73","doi-asserted-by":"crossref","unstructured":"Dullius AAS, Fava SMCL, Ribeiro PM, Terra FS. Alcohol consumption\/dependence and resilience in older adults with high blood pressure. Revista Latino-Americana de Enfermagem, 2018; 26.","DOI":"10.1590\/1518-8345.2466.3024"},{"issue":"9","key":"5450_CR74","doi-asserted-by":"crossref","first-page":"1670","DOI":"10.1111\/add.14703","volume":"114","author":"E Day","year":"2019","unstructured":"Day E, Rudd JH. Alcohol use disorders and the heart. Addiction. 2019;114(9):1670\u20138.","journal-title":"Addiction"},{"key":"5450_CR75","doi-asserted-by":"crossref","unstructured":"Ebrahimi A, Wiil UK, Mansourvar M, Naemi A, Andersen K, Nielsen AS. Analysis of comorbidities of alcohol use disorder. In: 2021 IEEE symposium on computers and communications (ISCC), IEEE; 2021, pp. 1\u20137.","DOI":"10.1109\/ISCC53001.2021.9631512"},{"issue":"4","key":"5450_CR76","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1109\/TKDE.2005.66","volume":"17","author":"H Liu","year":"2005","unstructured":"Liu H, Yu L. Toward integrating feature selection algorithms for classification and clustering. IEEE Trans Knowl Data Eng. 2005;17(4):491\u2013502.","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"1","key":"5450_CR77","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1007\/s13679-014-0129-4","volume":"4","author":"G Traversy","year":"2015","unstructured":"Traversy G, Chaput J-P. Alcohol consumption and obesity: an update. Curr Obes Rep. 2015;4(1):122\u201330.","journal-title":"Curr Obes Rep"},{"issue":"5","key":"5450_CR78","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1038\/sj.ijo.0802874","volume":"29","author":"J Tolstrup","year":"2005","unstructured":"Tolstrup J, Heitmann B, Tj\u00f8nneland A, Overvad O, S\u00f8rensen T, Gr\u00f8nbaek M. The relation between drinking pattern and body mass index and waist and hip circumference. Int J Obes. 2005;29(5):490\u20137.","journal-title":"Int J Obes"},{"issue":"3","key":"5450_CR79","doi-asserted-by":"crossref","first-page":"636","DOI":"10.5740\/jaoacint.SGE_Goodarzi","volume":"95","author":"M Goodarzi","year":"2012","unstructured":"Goodarzi M, Dejaegher B, Heyden YV. Feature selection methods in QSAR studies. J AOAC Int. 2012;95(3):636\u201351.","journal-title":"J AOAC Int"},{"key":"5450_CR80","unstructured":"Lundberg SM, Lee SI. A unified approach to interpreting model predictions. In: 31st Annual Conference on Neural Information Processing Systems (NIPS), 2017, p. 4765\u20134774."},{"key":"5450_CR81","doi-asserted-by":"crossref","unstructured":"Ribeiro MT, Singh S, Guestrin C. Why should i trust you? Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining; 2016, pp. 1135\u20131144.","DOI":"10.1145\/2939672.2939778"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-023-05450-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-023-05450-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-023-05450-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,18]],"date-time":"2023-11-18T13:09:04Z","timestamp":1700312944000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-023-05450-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,2]]},"references-count":81,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["5450"],"URL":"https:\/\/doi.org\/10.1186\/s12859-023-05450-6","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,2]]},"assertion":[{"value":"20 January 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 August 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 September 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"According to Executive Order (LBK) No. 1188 of 24 September 2016 on the Danish Health Act (Sundhedsloven) \u00a7 46, Sects.\u00a0\"\" and \"\", information about individuals' health conditions, other purely private matters and other confidential information from patient records can, when the research project is not covered by the Act on a scientific ethics committee system and treatment of biomedical research projects, can be passed on to a researcher for use in a concrete research project of significant societal importance interest after approval by the Agency for Patient Safety, which determines conditions for the transfer. Therefore, collection of the data in the Relay study was approved by the Danish Data Protection Agency (The Region of Southern Denmark project-ID 2008-58-0035). The Review Board at the Regional Scientific Ethical Committees of Southern Denmark decided that a formal informed consent was not required the patients as the study was considered a register study that did not entail an intervention (Project ID: S-20130084). Collection of data from the Electronic Health Records was approved by the Danish Patient Safety Authority (Project-ID 3-3013-1601\/1) and the Danish Data Protection Agency (The Region of Southern Denmark Project-ID 16\/12126). The authors confirm that all methods were carried out in accordance with relevant guidelines and regulations.","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 that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"329"}}