{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,26]],"date-time":"2026-04-26T04:51:10Z","timestamp":1777179070421,"version":"3.51.4"},"reference-count":31,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,5,13]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The use of decision trees and artificial neural networks (ANNs) in health-care research is widespread, as they enable health-care providers with the tools they need to make better medical decisions with their patients. ANNs specifically are extremely helpful in predictive research as they can provide investigators with knowledge about future trends and patterns. However, a major downside to ANNs is their lack of interpretability. Understandability of the model is important as it ensures the outcomes are true to the dataset\u2019s original labels and are not impacted by algorithmic bias. In comparison, decision trees map out their entire process before providing the results, which leads to a higher level of trust in the model and the conclusions it supplies the investigators with. This is essential as many historical datasets lack equal and fair representation of all races and sexes, which might directly correlate to a lesser treatment given to females and individuals in minority groups. Here, we review existing work around the differences and connections between ANNs and decision trees with implications for research in health care.<\/jats:p>","DOI":"10.1515\/comp-2022-0279","type":"journal-article","created":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T12:20:46Z","timestamp":1715602846000},"source":"Crossref","is-referenced-by-count":8,"title":["The use of artificial neural networks and decision trees: Implications for health-care research"],"prefix":"10.1515","volume":"14","author":[{"given":"Shaina","family":"Smith","sequence":"first","affiliation":[{"name":"Department of Computer Science, Trent University , Peterborough , Ontario , Canada"}]},{"given":"Sabine","family":"McConnell","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Trent University , Peterborough , Ontario , Canada"}]}],"member":"374","published-online":{"date-parts":[[2024,5,13]]},"reference":[{"key":"2024051312202968241_j_comp-2022-0279_ref_001","doi-asserted-by":"crossref","unstructured":"M. Gandhi and S. N. Singh, \u201cPredictions in heart disease using techniques of data mining,\u201d in: 2015 International Conference on Futuristic Trends on Computational Analysis and Knowledge Management (ABLAZE), 2015, pp. 520\u2013525.","DOI":"10.1109\/ABLAZE.2015.7154917"},{"key":"2024051312202968241_j_comp-2022-0279_ref_002","unstructured":"D. Dancey, D. A. McLean, and Z. A. Bandar, Decision tree extraction from trained neural networks, American Association for Artificial Intelligence, 2004."},{"key":"2024051312202968241_j_comp-2022-0279_ref_003","doi-asserted-by":"crossref","unstructured":"M. Ghassemi, L. Oakden-Rayner, and A. L. Beam, \u201cThe false hope of current approaches to explainable artificial intelligence in health care,\u201d The Lancet, vol. 3, pp. 745\u2013750, 2021.","DOI":"10.1016\/S2589-7500(21)00208-9"},{"key":"2024051312202968241_j_comp-2022-0279_ref_004","doi-asserted-by":"crossref","unstructured":"A. Rai, \u201cExplainable AI: From black box to glass box,\u201d J. Acad. Marketing Sci., vol. 48, no. 1, pp. 137\u2013141, 2020.","DOI":"10.1007\/s11747-019-00710-5"},{"key":"2024051312202968241_j_comp-2022-0279_ref_005","doi-asserted-by":"crossref","unstructured":"W. S. McCulloch and W. H. Pitts, \u201cA logical calculus of the ideas immanent in nervous activity,\u201d Bulletin of Mathematical Biophysics, vol. 5, pp. 115\u2013133, 1943.","DOI":"10.1007\/BF02478259"},{"key":"2024051312202968241_j_comp-2022-0279_ref_006","doi-asserted-by":"crossref","unstructured":"F. Rosenblatt, \u201cThe perceptron: A probabilistic model for information storage and organization in the brain,\u201d Psychological Review, vol. 65, no. 6, pp. 386\u2013408, 1958. 10.1037\/h0042519.","DOI":"10.1037\/h0042519"},{"key":"2024051312202968241_j_comp-2022-0279_ref_007","doi-asserted-by":"crossref","unstructured":"P. Charilaou and R. Battat, \u201cMachine learning models and over-fitting considerations,\u201d World J. Gastroenterol., vol. 28, no. 5, pp. 605\u2013607, 2022. 10.3748\/wjg.v28.i5.605. PMID: 35316964; PMCID: PMC8905023.","DOI":"10.3748\/wjg.v28.i5.605"},{"key":"2024051312202968241_j_comp-2022-0279_ref_008","unstructured":"P. Tan, M. Steinbach, V. Kumar, and A. Karpatne, Introduction to data mining. Pearson Education, New York, NY, 2019."},{"key":"2024051312202968241_j_comp-2022-0279_ref_009","unstructured":"N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, \u201cDropout: a simple way to prevent neural networks from overfitting,\u201d J. Machine Learn. Res., vol. 15, pp. 1929\u20131958, 2014."},{"key":"2024051312202968241_j_comp-2022-0279_ref_010","doi-asserted-by":"crossref","unstructured":"J. R. Quinlan, \u201cInduction of decision trees,\u201d Machine Learning, vol. 1, no. 1, pp. 81\u2013106, 1986.","DOI":"10.1007\/BF00116251"},{"key":"2024051312202968241_j_comp-2022-0279_ref_011","doi-asserted-by":"crossref","unstructured":"N. Shahid, T. Rappon, and W. Berta, \u201cApplications of artificial neural networks in health care organizational decision-making: A scoping review,\u201d PLoS ONE, vol. 14, no. 2, e0212356, 2019.","DOI":"10.1371\/journal.pone.0212356"},{"key":"2024051312202968241_j_comp-2022-0279_ref_012","doi-asserted-by":"crossref","unstructured":"M. J. Montbriand, \u201cDecision tree model describing alternate health care choices made by oncology patients,\u201d Cancer Nursing, vol. 18, no. 2, p. 117, 1995.","DOI":"10.1097\/00002820-199504000-00004"},{"key":"2024051312202968241_j_comp-2022-0279_ref_013","doi-asserted-by":"crossref","unstructured":"M. Giotta, P. Trerotoli, V. O. Palmieri, F. Passerini, P. Portincasa, I. Dargenio, et al., \u201cApplication of a decision tree model to predict the outcome of non-intensive inpatients hospitalized for COVID-19,\u201d Int. J. Environ. Res. Public Health, vol. 19, no. 20, p. 13016, 2022. 10.3390\/ijerph192013016. PMID: 36293594; PMCID: PMC9602523.","DOI":"10.3390\/ijerph192013016"},{"key":"2024051312202968241_j_comp-2022-0279_ref_014","doi-asserted-by":"crossref","unstructured":"C. Chern, Y. Chen, and B. Hsiao, \u201cDecision tree-based classifier in providing telehealth service,\u201d BMC Medical Inform. Decision Making, vol. 19, no. 104, 2019.","DOI":"10.1186\/s12911-019-0825-9"},{"key":"2024051312202968241_j_comp-2022-0279_ref_015","doi-asserted-by":"crossref","unstructured":"J. Bae, \u201cThe clinical decision analysis using decision tree,\u201d Epidemiol. Health, vol. 36, e2014025, 2014.","DOI":"10.4178\/epih\/e2014025"},{"key":"2024051312202968241_j_comp-2022-0279_ref_016","unstructured":"O. Boz, Converting a trained neural network to a decision tree dectext - decision tree extractor. Ph.D. Dissertation. Lehigh University, USA. Advisor(s) Donald Hillman. Order Number: AAI9982861, 2000."},{"key":"2024051312202968241_j_comp-2022-0279_ref_017","doi-asserted-by":"crossref","unstructured":"F. Bretz, W. Maurer, and D. Xi, \u201cReplicability, reproducibility, and multiplicity in drug development,\u201d Chance, vol. 32, no. 4, pp. 4\u201311, 2019.","DOI":"10.1080\/09332480.2019.1695432"},{"key":"2024051312202968241_j_comp-2022-0279_ref_018","doi-asserted-by":"crossref","unstructured":"Z. Obermeyer, B. Powers, C. Vogeli, and S. Mullainathan, \u201cDissecting racial bias in an algorithm used to manage the health of populations,\u201d Science, vol. 366, no. 6464, pp. 447\u2013452, 2019.","DOI":"10.1126\/science.aax2342"},{"key":"2024051312202968241_j_comp-2022-0279_ref_019","doi-asserted-by":"crossref","unstructured":"D. R. Williams and C. Collings, \u201cRacial residential segregation: A fundamental cause of racial disparities in health,\u201d Public Health Reports, vol. 116, no. 5, pp. 414\u201341, 2001.","DOI":"10.1093\/phr\/116.5.404"},{"key":"2024051312202968241_j_comp-2022-0279_ref_020","doi-asserted-by":"crossref","unstructured":"R. Dresser, \u201cWanted single, white male for medical research,\u201d The Hastings Center Report, vol. 22, no. 1, pp. 24\u201329, 1992.","DOI":"10.2307\/3562720"},{"key":"2024051312202968241_j_comp-2022-0279_ref_021","doi-asserted-by":"crossref","unstructured":"S. L. Klein and K. L. Flanagan, \u201cSex differences in immune responses,\u201d Nature Reviews Immunology, vol. 16, pp. 626\u2013638, 2016.","DOI":"10.1038\/nri.2016.90"},{"key":"2024051312202968241_j_comp-2022-0279_ref_022","doi-asserted-by":"crossref","unstructured":"D. Westergaard, P. Moseley, F. K. H. S\u00f8rup, P. Baldi, and S. Brunak, \u201cPopulation-wide analysis of differences in disease progression patterns in men and women,\u201d Nature Commun., vol. 10, no. 666, pp. 1143\u20131148, 2019.","DOI":"10.1038\/s41467-019-08475-9"},{"key":"2024051312202968241_j_comp-2022-0279_ref_023","doi-asserted-by":"crossref","unstructured":"P. O. Quinn and M. Madhoo, \u201cA review of attention-deficit\/hyperactivity disorder in women and girls: Uncovering this hidden diagnosis,\u201d The Primary Care Companion for CNS Disorders, vol. 16, no. 3, PCC.13r01596, 2014.","DOI":"10.4088\/PCC.13r01596"},{"key":"2024051312202968241_j_comp-2022-0279_ref_024","doi-asserted-by":"crossref","unstructured":"O. P. Soldin and D. R. Mattison, \u201cSex differences in pharmacokinetics and pharmacodynamics,\u201d Clin. Pharmacokinetics, vol. 48, no. 3, pp. 143\u2013157, 2009.","DOI":"10.2165\/00003088-200948030-00001"},{"key":"2024051312202968241_j_comp-2022-0279_ref_025","unstructured":"H. J. Geiger, \u201cRacial and ethnic disparities in diagnosis and treatment: A review of the evidence and a consideration of causes,\u201d Washington (DC): National Academies Press (US), 2003."},{"key":"2024051312202968241_j_comp-2022-0279_ref_026","doi-asserted-by":"crossref","unstructured":"C FitzGerald and S. Hurst, \u201cImplicit bias in healthcare professionals: a systematic review,\u201d BMC Med. Ethics, vol. 18, pp. 19\u201319, 2017.","DOI":"10.1186\/s12910-017-0179-8"},{"key":"2024051312202968241_j_comp-2022-0279_ref_027","doi-asserted-by":"crossref","unstructured":"J. H. Jessica, \u201cAddressing health disparities by addressing structural racism and implicit bias in nursing education,\u201d Nurse Education Today, 121, p. 105670, 2023.","DOI":"10.1016\/j.nedt.2022.105670"},{"key":"2024051312202968241_j_comp-2022-0279_ref_028","doi-asserted-by":"crossref","unstructured":"N. C. Woitowich, A. Beery, and T. Woodruff, \u201cA 10-year follow-up study of sex inclusion in the biological sciences,\u201d eLife, vol. 9, p. e56344, 2020.","DOI":"10.7554\/eLife.56344"},{"key":"2024051312202968241_j_comp-2022-0279_ref_029","doi-asserted-by":"crossref","unstructured":"A. K. Beery and I. Zucker, \u201cSex bias in neuroscience and biomedical research,\u201d Neurosci. Biobehav. Rev., vol. 35, no. 3, pp. 565\u2013572, 2011.","DOI":"10.1016\/j.neubiorev.2010.07.002"},{"key":"2024051312202968241_j_comp-2022-0279_ref_030","unstructured":"D. R. Williams and T. D. Rucker, \u201cUnderstanding and addressing racial disparities in health care,\u201d Health Care Financing Review, vol. 21, no. 4, pp. 75\u201390, 2000."},{"key":"2024051312202968241_j_comp-2022-0279_ref_031","unstructured":"European Commission. White Paper on Artificial Intelligence: A European Approach to Excellence and Trust, 2020."}],"container-title":["Open Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/comp-2022-0279\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/comp-2022-0279\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T12:21:10Z","timestamp":1715602870000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/comp-2022-0279\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,1]]},"references-count":31,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,5,13]]},"published-print":{"date-parts":[[2024,5,13]]}},"alternative-id":["10.1515\/comp-2022-0279"],"URL":"https:\/\/doi.org\/10.1515\/comp-2022-0279","relation":{},"ISSN":["2299-1093"],"issn-type":[{"value":"2299-1093","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,1]]},"article-number":"20220279"}}