{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T17:08:28Z","timestamp":1773248908343,"version":"3.50.1"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,7,24]],"date-time":"2022-07-24T00:00:00Z","timestamp":1658620800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,7,24]],"date-time":"2022-07-24T00:00:00Z","timestamp":1658620800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Due to the high mortality of COVID-19 patients, the use of a high-precision classification model of patient\u2019s mortality that is also interpretable, could help reduce mortality and take appropriate action urgently. In this study, the random forest method was used to select the effective features in COVID-19 mortality and the classification was performed using logistic model tree (LMT), classification and regression tree (CART), C4.5, and C5.0 tree based on important features.\n<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>In this retrospective study, the data of 2470 COVID-19 patients admitted to hospitals in Hamadan, west Iran, were used, of which 75.02% recovered and 24.98% died. To classify, at first among the 25 demographic, clinical, and laboratory findings, features with a relative importance more than 6% were selected by random forest. Then LMT, C4.5, C5.0, and CART trees were developed and the accuracy of classification performance was evaluated with recall, accuracy, and F1-score criteria for training, test, and total datasets. At last, the best tree was developed and the receiver operating characteristic curve and area under the curve (AUC) value were reported.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The results of this study showed that among demographic and clinical features gender and age, and among laboratory findings blood urea nitrogen, partial thromboplastin time, serum glutamic-oxaloacetic transaminase, and erythrocyte sedimentation rate had more than 6% relative importance. Developing the trees using the above features revealed that the CART with the values of F1-score, Accuracy, and Recall, 0.8681, 0.7824, and 0.955, respectively, for the test dataset and 0.8667, 0.7834, and 0.9385, respectively, for the total dataset had the best performance. The AUC value obtained for the CART was 79.5%.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>Finding a highly accurate and qualified model for interpreting the classification of a response that is considered clinically consequential is critical at all stages, including treatment and immediate decision making. In this study, the CART with its high accuracy for diagnosing and classifying mortality of COVID-19 patients as well as prioritizing important demographic, clinical, and laboratory findings in an interpretable format, risk factors for prognosis of COVID-19 patients mortality identify and enable immediate and appropriate decisions for health professionals and physicians.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-022-01939-x","type":"journal-article","created":{"date-parts":[[2022,7,24]],"date-time":"2022-07-24T14:02:22Z","timestamp":1658671342000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Application of machine learning models based on decision trees in classifying the factors affecting mortality of COVID-19 patients in Hamadan, Iran"],"prefix":"10.1186","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1597-7327","authenticated-orcid":false,"given":"Samad","family":"Moslehi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4447-2080","authenticated-orcid":false,"given":"Niloofar","family":"Rabiei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7483-3502","authenticated-orcid":false,"given":"Ali Reza","family":"Soltanian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5802-6367","authenticated-orcid":false,"given":"Mojgan","family":"Mamani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,7,24]]},"reference":[{"key":"1939_CR1","unstructured":"WHO. https:\/\/www.who.int\/emergencies\/diseases\/novel-coronavirus-2019\/situation-reports. 2022."},{"issue":"1","key":"1939_CR2","first-page":"34","volume":"122","author":"M Javanian","year":"2021","unstructured":"Javanian M, Bayani M, Shokri M, Sadeghi-Haddad-Zavareh M, Babazadeh A, Ghadimi R, et al. Risk factors for mortality of 557 adult patients with COVID 19 in Babol, Northern Iran: a retrospective cohort study. Bratisl Lek Listy. 2021;122(1):34\u20138.","journal-title":"Bratisl Lek Listy"},{"issue":"1","key":"1939_CR3","doi-asserted-by":"publisher","first-page":"474","DOI":"10.1186\/s12879-021-06179-4","volume":"21","author":"M-R Sohrabi","year":"2021","unstructured":"Sohrabi M-R, Amin R, Maher A, Bahadorimonfared A, Janbazi S, Hannani K, et al. Sociodemographic determinants and clinical risk factors associated with COVID-19 severity: a cross-sectional analysis of over 200,000 patients in Tehran, Iran. BMC Infect Dis. 2021;21(1):474.","journal-title":"BMC Infect Dis"},{"key":"1939_CR4","doi-asserted-by":"publisher","first-page":"2126","DOI":"10.1002\/jmv.27594","volume":"94","author":"ED Esmaeili","year":"2022","unstructured":"Esmaeili ED, Fakhari A, Naghili B, Khodamoradi F, Azizi H. Case fatality, mortality, socio-demographic, and screening of COVID-19 in the elderly population: a population-based registry study in Iran. J Med Virol. 2022;94:2126\u201332.","journal-title":"J Med Virol"},{"key":"1939_CR5","first-page":"1","volume":"55","author":"S Shah","year":"2021","unstructured":"Shah S, Mulahuwaish A, Ghafoor KZ, Maghdid HS. Prediction of global spread of COVID-19 pandemic: a review and research challenges. Artif Intell Rev. 2021;55:1\u201322.","journal-title":"Artif Intell Rev"},{"issue":"10223","key":"1939_CR6","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1016\/S0140-6736(20)30183-5","volume":"395","author":"W Wu","year":"2020","unstructured":"Wu W, Wang A, Liu M. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395(10223):497\u2013506.","journal-title":"Lancet"},{"issue":"6","key":"1939_CR7","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1080\/10408363.2020.1770685","volume":"57","author":"G Ponti","year":"2020","unstructured":"Ponti G, Maccaferri M, Ruini C, Tomasi A, Ozben T. Biomarkers associated with COVID-19 disease progression. Crit Rev Clin Lab Sci. 2020;57(6):389\u201399.","journal-title":"Crit Rev Clin Lab Sci"},{"key":"1939_CR8","doi-asserted-by":"publisher","first-page":"758","DOI":"10.3389\/fimmu.2021.646095","volume":"12","author":"A Copaescu","year":"2021","unstructured":"Copaescu A, James F, Mouhtouris E, Vogrin S, Smibert OC, Gordon CL, et al. The role of immunological and clinical biomarkers to predict clinical COVID-19 severity and response to therapy\u2014a prospective longitudinal study. Front Immunol. 2021;12:758.","journal-title":"Front Immunol"},{"key":"1939_CR9","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1016\/j.amsu.2020.12.035","volume":"62","author":"S Taj","year":"2021","unstructured":"Taj S, Fatima SA, Imran S, Lone A, Ahmed Q. Role of hematological parameters in the stratification of COVID-19 disease severity. Ann Med Surg. 2021;62:68\u201372.","journal-title":"Ann Med Surg"},{"issue":"2","key":"1939_CR10","first-page":"154","volume":"7","author":"M Shanbehzadeh","year":"2021","unstructured":"Shanbehzadeh M, Orooji A, Kazemi Arpanahi H. Comparing of data mining techniques for predicting in-hospital mortality among patients with Covid-19. J Biostat Epidemiol. 2021;7(2):154\u201373.","journal-title":"J Biostat Epidemiol."},{"issue":"2","key":"1939_CR11","first-page":"1","volume":"22","author":"K Moulaei","year":"2022","unstructured":"Moulaei K, Shanbehzadeh M, Mohammadi-Taghiabad Z, Kazemi-Arpanahi H. Comparing machine learning algorithms for predicting COVID-19 mortality. BMC Med Inform Decis Mak. 2022;22(2):1\u20132.","journal-title":"BMC Med Inform Decis Mak"},{"key":"1939_CR12","first-page":"91","volume-title":"Diagnosing COVID-19 lung inflammation using machine learning algorithms: a comparative study. Internet of Medical Things for Smart Healthcare","author":"AM Ali","year":"2020","unstructured":"Ali AM, Ghafoor KZ, Maghdid HS, Mulahuwaish A. Diagnosing COVID-19 lung inflammation using machine learning algorithms: a comparative study. Internet of Medical Things for Smart Healthcare. Berlin: Springer; 2020. p. 91\u2013105."},{"key":"1939_CR13","unstructured":"Ali AM, Ghafoor KZ, Mulahuwaish A, Halgurd S, Mohammed MA. COVID-19 pneumonia level detection using deep learning algorithm."},{"key":"1939_CR14","doi-asserted-by":"crossref","unstructured":"Yadaw AS, Li Y-C, Bose S, Iyengar R, Bunyavanich S, Pandey G. Clinical predictors of COVID-19 mortality. medRxiv. 2020.","DOI":"10.1101\/2020.05.19.20103036"},{"key":"1939_CR15","doi-asserted-by":"crossref","unstructured":"de Moraes Batista AF, Miraglia JL, Donato THR, Chiavegatto Filho ADP. COVID-19 diagnosis prediction in emergency care patients: a machine learning approach. medRxiv. 2020.","DOI":"10.1101\/2020.04.04.20052092"},{"key":"1939_CR16","doi-asserted-by":"publisher","first-page":"104335","DOI":"10.1016\/j.compbiomed.2021.104335","volume":"132","author":"MA Alves","year":"2021","unstructured":"Alves MA, Castro GZ, Oliveira BAS, Ferreira LA, Ram\u00edrez JA, Silva R, et al. Explaining machine learning based diagnosis of COVID-19 from routine blood tests with decision trees and criteria graphs. Comput Biol Med. 2021;132:104335.","journal-title":"Comput Biol Med"},{"key":"1939_CR17","doi-asserted-by":"crossref","unstructured":"Meng Z, Wang M, Song H, Guo S, Zhou Y, Li W, et al. Development and utilization of an intelligent application for aiding COVID-19 diagnosis. medRxiv. 2020.","DOI":"10.1101\/2020.03.18.20035816"},{"key":"1939_CR18","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1016\/j.cca.2020.08.019","volume":"510","author":"A Pourbagheri-Sigaroodi","year":"2020","unstructured":"Pourbagheri-Sigaroodi A, Bashash D, Fateh F, Abolghasemi H. Laboratory findings in COVID-19 diagnosis and prognosis. Clin Chim Acta Int J Clin Chem. 2020;510:475.","journal-title":"Clin Chim Acta Int J Clin Chem"},{"key":"1939_CR19","volume-title":"Data mining with decision trees: theory and applications","author":"OZ Maimon","year":"2014","unstructured":"Maimon OZ, Rokach L. Data mining with decision trees: theory and applications. Singapore: World Scientific; 2014."},{"key":"1939_CR20","doi-asserted-by":"publisher","first-page":"58706","DOI":"10.1109\/ACCESS.2021.3073215","volume":"9","author":"F Es-Sabery","year":"2021","unstructured":"Es-Sabery F, Es-Sabery K, Qadir J, Sainz-De-Abajo B, Hair A, Garc\u00eda-Zapirain B, et al. A mapreduce opinion mining for COVID-19-related tweets classification using enhanced ID3 decision tree classifier. IEEE Access. 2021;9:58706\u201339.","journal-title":"IEEE Access"},{"key":"1939_CR21","doi-asserted-by":"publisher","first-page":"104089","DOI":"10.1016\/j.compbiomed.2020.104089","volume":"128","author":"MM Ghiasi","year":"2021","unstructured":"Ghiasi MM, Zendehboudi S. Application of decision tree-based ensemble learning in the classification of breast cancer. Comput Biol Med. 2021;128:104089.","journal-title":"Comput Biol Med"},{"key":"1939_CR22","doi-asserted-by":"crossref","unstructured":"Talebi A, Borumandnia N, Jafari R, Pourhoseingholi MA, Jafari NJ, Ashtari S, et al. Predicting the COVID-19 patients\u2019 status using chest CT scan findings: a risk assessment model based on Decision tree. 2021.","DOI":"10.21203\/rs.3.rs-56387\/v3"},{"issue":"1","key":"1939_CR23","first-page":"3","volume":"20","author":"M Schonlau","year":"2020","unstructured":"Schonlau M, Zou RY. The random forest algorithm for statistical learning. Stand Genomic Sci. 2020;20(1):3\u201329.","journal-title":"Stand Genomic Sci"},{"issue":"2","key":"1939_CR24","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1080\/08839514.2018.1447479","volume":"32","author":"A Cherfi","year":"2018","unstructured":"Cherfi A, Nouira K, Ferchichi A. Very fast C4.5 decision tree algorithm. Appl Artif Intell. 2018;32(2):119\u201337.","journal-title":"Appl Artif Intell"},{"issue":"1","key":"1939_CR25","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1007\/s10994-005-0466-3","volume":"59","author":"N Landwehr","year":"2005","unstructured":"Landwehr N, Hall M, Frank E. Logistic model trees. Mach Learn. 2005;59(1):161\u2013205.","journal-title":"Mach Learn"},{"issue":"11","key":"1939_CR26","doi-asserted-by":"publisher","first-page":"1217","DOI":"10.15537\/smj.2020.11.25495","volume":"41","author":"SI Abohamr","year":"2020","unstructured":"Abohamr SI, Abazid RM, Aldossari MA, Amer HA, Badhawi OS, Aljunaidi OM, et al. Clinical characteristics and in-hospital mortality of COVID-19 adult patients in Saudi Arabia. Saudi Med J. 2020;41(11):1217.","journal-title":"Saudi Med J"},{"key":"1939_CR27","doi-asserted-by":"crossref","unstructured":"Shamrat FJM, Ranjan R, Md K, Hasib AY, Siddique AH. Performance evaluation among ID3, C4. 5, and CART decision tree algorithms. In: Pervasive computing and social networking: proceedings of ICPCSN 2021, p. 127. 2021.","DOI":"10.1007\/978-981-16-5640-8_11"},{"issue":"16","key":"1939_CR28","first-page":"18","volume":"117","author":"R Pandya","year":"2015","unstructured":"Pandya R, Pandya J. C5. 0 algorithm to improved decision tree with feature selection and reduced error pruning. Int J Comput Appl. 2015;117(16):18\u201321.","journal-title":"Int J Comput Appl"},{"key":"1939_CR29","doi-asserted-by":"crossref","unstructured":"Yacouby R, Axman D, editors. Probabilistic extension of precision, recall, and f1 score for more thorough evaluation of classification models. In: Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems; 2020.","DOI":"10.18653\/v1\/2020.eval4nlp-1.9"},{"issue":"3","key":"1939_CR30","doi-asserted-by":"publisher","first-page":"31","DOI":"10.3390\/computers10030031","volume":"10","author":"A Alotaibi","year":"2021","unstructured":"Alotaibi A, Shiblee M, Alshahrani A. Prediction of severity of COVID-19-infected patients using machine learning techniques. Computers. 2021;10(3):31.","journal-title":"Computers"},{"key":"1939_CR31","doi-asserted-by":"publisher","first-page":"e9945","DOI":"10.7717\/peerj.9945","volume":"8","author":"J Wang","year":"2020","unstructured":"Wang J, Yu H, Hua Q, Jing S, Liu Z, Peng X, et al. A descriptive study of random forest algorithm for predicting COVID-19 patients outcome. PeerJ. 2020;8:e9945.","journal-title":"PeerJ"},{"key":"1939_CR32","doi-asserted-by":"crossref","unstructured":"Cao M, Zhang D, Wang Y, Lu Y, Zhu X, Li Y, et al. Clinical features of patients infected with the 2019 novel coronavirus (COVID-19) in Shanghai, China. MedRxiv. 2020.","DOI":"10.1101\/2020.03.04.20030395"},{"issue":"18","key":"1939_CR33","doi-asserted-by":"publisher","first-page":"1708","DOI":"10.1056\/NEJMoa2002032","volume":"382","author":"W-J Guan","year":"2020","unstructured":"Guan W-J, Ni Z-Y, Hu Y, Liang W-H, Ou C-Q, He J-X, et al. Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med. 2020;382(18):1708\u201320.","journal-title":"N Engl J Med"},{"issue":"15","key":"1939_CR34","doi-asserted-by":"publisher","first-page":"458","DOI":"10.15585\/mmwr.mm6915e3","volume":"69","author":"S Garg","year":"2020","unstructured":"Garg S, Kim L, Whitaker M, O\u2019Halloran A, Cummings C, Holstein R, et al. Hospitalization rates and characteristics of patients hospitalized with laboratory-confirmed coronavirus disease 2019\u2014COVID-NET, 14 States, March 1\u201330, 2020. Morb Mortal Wkly Rep. 2020;69(15):458.","journal-title":"Morb Mortal Wkly Rep"},{"key":"1939_CR35","doi-asserted-by":"publisher","first-page":"104378","DOI":"10.1016\/j.jcv.2020.104378","volume":"127","author":"M Nikpouraghdam","year":"2020","unstructured":"Nikpouraghdam M, Farahani AJ, Alishiri G, Heydari S, Ebrahimnia M, Samadinia H, et al. Epidemiological characteristics of coronavirus disease 2019 (COVID-19) patients in IRAN: a single center study. J Clin Virol. 2020;127:104378.","journal-title":"J Clin Virol"},{"key":"1939_CR36","doi-asserted-by":"crossref","unstructured":"Li S, Lin Y, Zhu T, Fan M, Xu S, Qiu W, et al. Development and external evaluation of predictions models for mortality of COVID-19 patients using machine learning method. Neural Comput Appl. 2021:1\u201310.","DOI":"10.1007\/s00521-020-05592-1"},{"issue":"10","key":"1939_CR37","doi-asserted-by":"publisher","first-page":"e23475","DOI":"10.1002\/jcla.23475","volume":"34","author":"J Peng","year":"2020","unstructured":"Peng J, Qi D, Yuan G, Deng X, Mei Y, Feng L, et al. Diagnostic value of peripheral hematologic markers for coronavirus disease 2019 (COVID-19): a multicenter, cross-sectional study. J Clin Lab Anal. 2020;34(10):e23475.","journal-title":"J Clin Lab Anal"},{"key":"1939_CR38","doi-asserted-by":"crossref","unstructured":"Bhatia S, Makhija Y, Jayaswal S, Singh S, Gupta I. Severity and mortality prediction models to triage Indian COVID-19 patients. 2021. arXiv:210902485.","DOI":"10.1371\/journal.pdig.0000020"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-022-01939-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-022-01939-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-022-01939-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,24]],"date-time":"2022-07-24T14:02:38Z","timestamp":1658671358000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-022-01939-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,24]]},"references-count":38,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["1939"],"URL":"https:\/\/doi.org\/10.1186\/s12911-022-01939-x","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,24]]},"assertion":[{"value":"8 March 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 July 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 July 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The procedures followed in this study strictly comply with the ethical standards formulated by the ethical committee of the hospitals in Iran. Ethical approval to start data collection and using the dataset in this study was elicited from the Ethical Committee of the Hamadan University of Medical Science with the approved ethical code: IR.UMSHA.REC.1400.891. Informed consent was obtained from all patients hospitalized or their legal guardian and patients under the age of 16 from parents\/legally authorized representatives. All the experiment protocols involving human data were in accordance with the University of Medical Science at Hamadan, Iran, privacy board, and internal review board guidelines.","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":"192"}}