IDEAS home Printed from https://ideas.repec.org/a/spr/infosf/vyid10.1007_s10796-019-09934-w.html
   My bibliography  Save this article

A Predictive Analytics-Based Decision Support System for Drug Courts

Author

Listed:
  • Hamed M. Zolbanin

    (University of Dayton)

  • Dursun Delen

    (Oklahoma State University)

  • Durand Crosby

    (Department of Mental Health and Substance Abuse Services)

  • David Wright

    (Department of Mental Health and Substance Abuse Services)

Abstract

This study employs predictive analytics to develop a decision support system for the prediction of recidivism in drug courts. Based on the input from subject matter experts, recidivism is defined as the violation of the treatment program requirements within three years after admission. We use two data processing methods to improve the accuracy of predictions: synthetic minority oversampling and survival data mining. The former creates a balanced data set and the latter boosts the model’s performance by adding several new, informative variables to the data set. After running several tree-based machine learning algorithms on the input data, random forest achieved the best performance (AUROC = 0.884, accuracy = 80.76%). Compared with the original data, oversampling and survival data mining increased AUROC by 0.068 and 0.018, respectively. Their combined contribution to AUROC was 0.088. We present a simplified version of decision rules and explain how the decision support system can be deployed. Therefore, this paper contributes to the analytics literature by illustrating how date/time variables - in applications where the response variable is defined as the occurrence of some event within a certain period - can be used in data management to improve the performance of predictive models and the resulting decision support systems.

Suggested Citation

  • Hamed M. Zolbanin & Dursun Delen & Durand Crosby & David Wright, 0. "A Predictive Analytics-Based Decision Support System for Drug Courts," Information Systems Frontiers, Springer, vol. 0, pages 1-20.
    • Handle: RePEc:spr:infosf:v::y::i::d:10.1007_s10796-019-09934-w
    DOI: 10.1007/s10796-019-09934-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10796-019-09934-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10796-019-09934-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Clyde Holsapple, 2008. "DSS Architecture and Types," International Handbooks on Information Systems, in: Handbook on Decision Support Systems 1, chapter 9, pages 163-189, Springer.
    2. Gerd Gigerenzer & Reinhard Selten (ed.), 2002. "Bounded Rationality: The Adaptive Toolbox," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262571641, December.
    3. Sniezek, Janet A. & Buckley, Timothy, 1995. "Cueing and Cognitive Conflict in Judge-Advisor Decision Making," Organizational Behavior and Human Decision Processes, Elsevier, vol. 62(2), pages 159-174, May.
    4. Mi (Jamie) Zhou & Baozhou Lu & Weiguo (Patrick) Fan & G. Alan Wang, 0. "Project description and crowdfunding success: an exploratory study," Information Systems Frontiers, Springer, vol. 0, pages 1-16.
    5. Frada Burstein & Clyde Holsapple, 2008. "Handbook on Decision Support Systems 2," International Handbooks on Information Systems, Springer, number 978-3-540-48716-6, November.
    6. Kyuhan Lee & Jinsoo Park & Iljoo Kim & Youngseok Choi, 0. "Predicting movie success with machine learning techniques: ways to improve accuracy," Information Systems Frontiers, Springer, vol. 0, pages 1-12.
    7. Ahmed Emam, 2015. "Intelligent drowsy eye detection using image mining," Information Systems Frontiers, Springer, vol. 17(4), pages 947-960, August.
    8. Sebastian Klenk & Jürgen Dippon & Peter Fritz & Gunther Heidemann, 2009. "Interactive survival analysis with the OCDM system: From development to application," Information Systems Frontiers, Springer, vol. 11(4), pages 391-403, September.
    9. Frada Burstein & Clyde Holsapple, 2008. "Handbook on Decision Support Systems 1," International Handbooks on Information Systems, Springer, number 978-3-540-48713-5, November.
    10. Brandon A. Beemer & Dawn G. Gregg, 2008. "Advisory Systems to Support Decision Making," International Handbooks on Information Systems, in: Handbook on Decision Support Systems 1, chapter 24, pages 511-527, Springer.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dursun Delen & Hamed M. Zolbanin & Durand Crosby & David Wright, 2021. "To imprison or not to imprison: an analytics model for drug courts," Annals of Operations Research, Springer, vol. 303(1), pages 101-124, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hamed M. Zolbanin & Dursun Delen & Durand Crosby & David Wright, 2020. "A Predictive Analytics-Based Decision Support System for Drug Courts," Information Systems Frontiers, Springer, vol. 22(6), pages 1323-1342, December.
    2. Smedberg, Henrik & Bandaru, Sunith, 2023. "Interactive knowledge discovery and knowledge visualization for decision support in multi-objective optimization," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1311-1329.
    3. Manuel Casal-Guisande & Alberto Comesaña-Campos & Alejandro Pereira & José-Benito Bouza-Rodríguez & Jorge Cerqueiro-Pequeño, 2022. "A Decision-Making Methodology Based on Expert Systems Applied to Machining Tools Condition Monitoring," Mathematics, MDPI, vol. 10(3), pages 1-30, February.
    4. Marta Kadłubek & Eleftherios Thalassinos & Joanna Domagała & Sandra Grabowska & Sebastian Saniuk, 2022. "Intelligent Transportation System Applications and Logistics Resources for Logistics Customer Service in Road Freight Transport Enterprises," Energies, MDPI, vol. 15(13), pages 1-27, June.
    5. Carolina Lino Martins & Pascale Zaraté & Adiel Teixeira de Almeida & Jônatas Araújo de Almeida & Danielle Costa Morais, 2021. "Web-Based DSS for Resource Allocation in Higher Education," International Journal of Decision Support System Technology (IJDSST), IGI Global, vol. 13(4), pages 1-23, October.
    6. Linkevičius, Edgaras & Borges, José G. & Doyle, Marie & Pülzl, Helga & Nordström, Eva-Maria & Vacik, Harald & Brukas, Vilis & Biber, Peter & Teder, Meelis & Kaimre, Paavo & Synek, Michal & Garcia-Gonz, 2019. "Linking forest policy issues and decision support tools in Europe," Forest Policy and Economics, Elsevier, vol. 103(C), pages 4-16.
    7. Fawad Ahmed & Yuan Jian Qin & Luis Martínez, 2019. "Sustainable Change Management through Employee Readiness: Decision Support System Adoption in Technology-Intensive British E-Businesses," Sustainability, MDPI, vol. 11(11), pages 1-28, May.
    8. N. Beridze & G. Janelidze, 2020. "Organizing Decision-Making Support System Based Multi-Dimensional Analysis of the Educational Process Data," Journal of ICT, Design, Engineering and Technological Science, Juhriyansyah Dalle, vol. 4(1), pages 1-5.
    9. necula, sabina-cristiana & Radu, Laura-Diana, 2011. "Decision Support Systems Usefulness and A Practical Solution Based on Semantic Web Technologies," MPRA Paper 51547, University Library of Munich, Germany.
    10. Ehsan Saqib & Gyozo Gidófalvi, 2024. "Dynamic Adaptive Charging Network Planning Under Deep Uncertainties," Energies, MDPI, vol. 17(21), pages 1-27, October.
    11. Pavlos Delias & Daniela Grigori, 2021. "Two Problem Formulations for Process Innovation Based on Operations Sophistication," International Journal of Decision Support System Technology (IJDSST), IGI Global, vol. 13(1), pages 1-18, January.
    12. Jorge Cerqueiro-Pequeño & Alberto Comesaña-Campos & Manuel Casal-Guisande & José-Benito Bouza-Rodríguez, 2020. "Design and Development of a New Methodology Based on Expert Systems Applied to the Prevention of Indoor Radon Gas Exposition Risks," IJERPH, MDPI, vol. 18(1), pages 1-32, December.
    13. Anthony William Bennett & Simone Müller Loose, 2024. "User-Centered Development of an Online Dashboard Tool for Economic Sustainability for Small and Medium Enterprises," Sustainability, MDPI, vol. 16(2), pages 1-24, January.
    14. Pavel Turčínek & Arnošt Motyčka, 2012. "Decision support system for promotion of Faculty of Business and Economics Mendel University," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 60(2), pages 443-448.
    15. Schillings, Christoph & Wanderer, Thomas & Cameron, Lachlan & van der Wal, Jan Tjalling & Jacquemin, Jerome & Veum, Karina, 2012. "A decision support system for assessing offshore wind energy potential in the North Sea," Energy Policy, Elsevier, vol. 49(C), pages 541-551.
    16. Kessel, Dany & Mollerstrom, Johanna & van Veldhuizen, Roel, 2021. "Can simple advice eliminate the gender gap in willingness to compete?," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 138, pages 1-1.
    17. Tyrychtr, J., 2017. "Analytical System with Decision Tree for Economic Benefit," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 9(4).
    18. Halbich, Cestmir & Vostrovsky, Vaclav, 2011. "GIS as spatial decision support system," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 3(2), June.
    19. Aspasia Tsaoussi & Eleni Zervogianni, 2010. "Judges as satisficers: a law and economics perspective on judicial liability," European Journal of Law and Economics, Springer, vol. 29(3), pages 333-357, June.
    20. Nicolas Marciales Parra, 2013. "A mathematical model for consumers based on aspiration adaptation theory and bounded rationality," Review of Applied Socio-Economic Research, Pro Global Science Association, vol. 5(1), pages 136-143, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:infosf:v::y::i::d:10.1007_s10796-019-09934-w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.