Advertisement

A Predictive Analytics-Based Decision Support System for Drug Courts

  • Hamed M. ZolbaninEmail author
  • Dursun Delen
  • Durand Crosby
  • David Wright
Article
  • 18 Downloads

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.

Keywords

Predictive analytics Survival data mining Machine learning Drug court 

Notes

References

  1. Allison, P. D. (2010). Survival Analysis Using SAS: A Practical Guide. Cary, NC: SAS Institute Inc.Google Scholar
  2. Barrett, J. P. (1974). The coefficient of determination – Some limitations. The American Statistician, 28(1), 19–20.Google Scholar
  3. Beemer, B. A., & Gregg, D. G. (2008). Advisory systems to support decision making. In Handbook on decision support systems (Vol. 1, pp. 511–527). Berlin Heidelberg: Springer.Google Scholar
  4. Belenko, S. R., Mara-Drita, I., & McElroy, J. E. (1992). Pre-arraignment drug tests in the pretrial release decision: Predicting defendant failure to appear. Crime & Delinquency, 38(4), 557–582.Google Scholar
  5. Benda, B. B., Toombs, N. J., & Peacock, M. (2002). Ecological factors in recidivism: A survival analysis of boot camp graduates after three years. Journal of Offender Rehabilitation, 35(1), 63–85.Google Scholar
  6. Bhattacharyya, S., Jha, S., Tharakunnel, K., & Westland, J. C. (2011). Data mining for credit card fraud: A comparative study. Decision Support Systems, 50(3), 602–613.Google Scholar
  7. Binswanger, I. A., Nowels, C., Corsi, K. F., Glanz, J., Long, J., Booth, R. E., & Steiner, J. F. (2012). Return to drug use and overdose after release from prison: a qualitative study of risk and protective factors. Addiction Science & Clinical Practice, 7(1), 3.Google Scholar
  8. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.Google Scholar
  9. Brynjolfsson, E., Hitt, L. M., & Kim, H. H. (2011). Strength in numbers: How does data-driven decision making affect firm performance? Retrieved from: http://ssrn.com/abstract = 1819486. Accessed 9 Dec 2015.
  10. Butzin, C. A., Saum, C. A., & Scarpitti, F. R. (2002). Factors associated with completion of a drug treatment court diversion program. Substance Use & Misuse, 37, 1615–1633.Google Scholar
  11. Chae, B. K., Yang, C., Olson, D., & Sheu, C. (2014). The impact of advanced analytics and data accuracy on operational performance: A contingent resource based theory (RBT) perspective. Decision Support Systems, 59, 119–126.Google Scholar
  12. Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16(1), 321–357.Google Scholar
  13. Chen, L., Li, X., Yang, Y., Kurniawati, H., Sheng, Q. Z., Hu, H. Y., & Huang, N. (2016). Personal health indexing based on medical examinations: A data mining approach. Decision Support Systems, 81, 54–65.Google Scholar
  14. Cissner, A. B., & Rempel, M. (2005). The state of drug court Research: Moving beyond “do they work?”. New York, NY: Center for Court Innovation.Google Scholar
  15. Cziko, G. A. (1989). Unpredictability and indeterminism in human behavior: Arguments and implications for educational research. Educational Researcher, 18(3), 17–25.Google Scholar
  16. Dag, A., Oztekin, A., Yucel, A., Bulur, S., & Megahed, F. M. (2017). Predicting heart transplantation outcomes through data analytics. Decision Support Systems, 94, 42–52.Google Scholar
  17. Davenport, T. H., & Harris, J. G. (2007). Competing on analytics: The new science of winning. Harvard Business Press.Google Scholar
  18. Despa, S. (2010). What is survival analysis. Cornell University, Cornell Statistical Consulting Unit, Newsletter. Retrieved from: https://www.cscu.cornell.edu/news/statnews/stnews78.pdf. Accessed 9 Dec 2015.
  19. Douzas, G., & Bacao, F. (2017). Self-organizing map oversampling (SOMO) for imbalanced data set learning. Expert Systems with Applications, 82, 40–52.Google Scholar
  20. Emam, A. (2015). Intelligent drowsy eye detection using image mining. Information Systems Frontiers, 17(4), 947–960.Google Scholar
  21. Erdahl, T. (2015). Survival analysis, recidivism, and booking data from the Stearns County jail.Google Scholar
  22. French, M. T., Zarkin, G. A., Hubbard, R. L., & Rachal, J. V. (1993). The effects of time in drug abuse treatment and employment on posttreatment drug use and criminal activity. The American Journal of Drug & Alcohol Abuse, 19(1), 19–33.Google Scholar
  23. Galante, R. (2015). Improving the performance of data mining models with data preparation using SAS® Enterprise miner. In Sao Paolo. Brazil: SAS Institute Inc.Google Scholar
  24. Ganganwar, V. (2012). An overview of classification algorithms for imbalanced datasets. International Journal of Emerging Technology and Advanced Engineering, 2, 42–47.Google Scholar
  25. Gigerenzer, G., & Selten, R. (2002). Bounded rationality: The adaptive toolbox. Cambridge, MA: MIT Press.Google Scholar
  26. Goldkamp, J. S., White, M. D., & Robinson, J. B. (2001). Do drug courts work? Getting inside the drug court black box. Journal of Drug Issues, 31(1), 27–72.Google Scholar
  27. Gottfredson, D. C., & Exum, M. L. (2002). The Baltimore city drug treatment court: One-year results from a randomized study. Journal of Research in Crime and Delinquency, 39(3), 337–356.Google Scholar
  28. Gutierrez, L., & Bourgon, G. (2009). Drug treatment courts: A quantitative review of study and treatment quality. Ottawa, Ontario: Public Safety Canada.Google Scholar
  29. Hartley, R. E., & Phillips, R. C. (2001). Who graduates from drug courts? Correlates of client success. American Journal of Criminal Justice, 26(1), 107–119.Google Scholar
  30. Hepburn, J. R., & Albonetti, C. A. (1994). Recidivism among drug offenders: A survival analysis of the effects of offender characteristics, type of offense, and two types of intervention. Journal of Quantitative Criminology, 10(2), 159–179.Google Scholar
  31. Hickert, A. O., Boyle, S. W., & Tollefson, D. R. (2009). Factors that predict drug court completion and drop out: Findings from an evaluation of salt Lake county's adult felony drug court. Journal of Social Service Research, 35(2), 149–162.Google Scholar
  32. Holsapple, C. W. (2008). DSS architecture and types. In Handbook on decision support systems (Vol. 1, pp. 163–189). Berlin Heidelberg: Springer.Google Scholar
  33. Holsapple, C., Lee-Post, A., & Pakath, R. (2014). A unified foundation for business analytics. Decision Support Systems, 64, 130–141.Google Scholar
  34. Hosmer, D., & Lemeshow, S. (1999). Applied survival analysis–regression modeling of time to event data. New York: John Wiley & Sons, Inc.Google Scholar
  35. Kaiser, K. A., & Holtfreter, K. (2016). An integrated theory of specialized court programs: Using procedural justice and therapeutic jurisprudence to promote offender compliance and rehabilitation. Criminal Justice and Behavior, 43(1), 45–62.Google Scholar
  36. Kearley, B. W. (2017). Long term effects of drug court participation: Evidence from a 15-year follow-up of a randomized controlled trial (Doctoral dissertation).Google Scholar
  37. Klenk, S., Dippon, J., Fritz, P., & Heidemann, G. (2009). Interactive survival analysis with the OCDM system: From development to application. Information Systems Frontiers, 11(4), 391–403.Google Scholar
  38. Kubrin, C. E., & Stewart, E. A. (2006). Predicting who reoffends: The neglected role of neighborhood context in recidivism studies. Criminology, 44(1), 165–197.Google Scholar
  39. Latimer, J., Morton-Bourgon, K., & Chretien, J. A. (2006). A meta-analytic examination of drug treatment courts: Do they reduce recidivism? Ottawa. Canada: Department of Justice.Google Scholar
  40. LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2011). Big data, analytics and the path from insights to value. MIT Sloan Management Review, 52(2), 21.Google Scholar
  41. Lee, T. Y. (2013). Survival data mining for big data: practitioner’s guide. Cary, NC: SAS Institute Inc.Google Scholar
  42. Lee, K., Park, J., Kim, I., & Choi, Y. (2016). Predicting movie success with machine learning techniques: Ways to improve accuracy. Information Systems Frontiers, 1–12.Google Scholar
  43. Listwan, S. J., Sundt, J. L., Holsinger, A. M., & Latessa, E. J. (2003). The effect of drug court programming on recidivism: The Cincinnati experience. Crime & Delinquency, 49(3), 389–411.Google Scholar
  44. Liu, P., Wang, Y., Cai, L., & Zhang, L. (2010). Classifying skewed data streams based on reusing data, in International Conference on Computer Application and System Modeling (ICCASM), pp. In V4–90-V4–93. Taiyuan: China.Google Scholar
  45. Marlowe, D. B. (2010). Research update on adult drug courts. Alexandria, VA: National Association of drug court professionals. Retrieved from: http://www.nadcp.org/sites/default/files/nadcp/Research%20Update%20on%20Adult%20Drug%20Courts%20-%20NADCP_1.pdf. Accessed 9 Dec 2015.
  46. Marlowe, D. B., Festinger, D. S., Lee, P. A., Dugosh, K. L., & Benasutti, K. M. (2006). Matching judicial supervision to clients’ risk status in drug court. Crime & Delinquency, 52(1), 52–76.Google Scholar
  47. Mauer, M. (2003). Comparative international rates of incarceration: An examination of causes and trends presented to the US Commission on civil rights (pp. 1–16). Washington, DC: The Sentencing Project Retrieved from: http://proxy.baremetal.com/november.org/stayinfo/breaking/Incarceration.pdf. Accessed 9 Dec 2015.
  48. McAfee, A., & Brynjolfsson, E. (2012). Big data: The management revolution. Harvard Business Review, 90(10), 60–68.Google Scholar
  49. Miethe, T. D., Lu, H., & Reese, E. (2000). Reintegrative shaming and recidivism risks in drug court: Explanations for some unexpected findings. Crime & Delinquency, 46(4), 522–541.Google Scholar
  50. Mitchell, O., Wilson, D. B., Eggers, A., & MacKenzie, D. L. (2012). Assessing the effectiveness of drug courts on recidivism: A meta-analytic review of traditional and non-traditional drug courts. Journal of Criminal Justice, 40(1), 60–71.Google Scholar
  51. Mullany, J. M., & Peat, B. (2008). Process evaluation of a county drug court: An analysis of descriptors, compliance and outcome—Answering some questions while raising others. Criminal Justice Policy Review, 19(4), 491–508.Google Scholar
  52. National Institute on Drug Abuse: What are hallucinogens. (2016, January). retrieved from: https://www.drugabuse.gov/publications/drugfacts/hallucinogens. Accessed 1 Sep 2017.
  53. Nekooeimehr, I., & Lai-Yuen, S. K. (2016). Adaptive semi-unsupervised weighted oversampling (A-SUWO) for imbalanced datasets. Expert Systems with Applications, 46, 405–416.Google Scholar
  54. Peters, R. H., & Murrin, M. R. (2000). Effectiveness of treatment-based drug courts in reducing criminal recidivism. Criminal Justice and Behavior, 27(1), 72–96.Google Scholar
  55. Piri, S., Delen, D., Liu, T., & Zolbanin, H. M. (2017). A data analytics approach to building a clinical decision support system for diabetic retinopathy: Developing and deploying a model ensemble. Decision Support Systems, forthcoming., 101, 12–27.  https://doi.org/10.1016/j.dss.2017.05.012.Google Scholar
  56. Potts, W. (2004). Survival data mining. Technical White paper, data miners. Retrieved from: https://pdfs.semanticscholar.org/7377/29c840e4dbaca00e4a4cae05a92124d5411c.pdf. Accessed 9 Dec 2015.
  57. Prendergast, M., Anglin, M. D., & Wellisch, J. (1995). Up to speed: Treatment for drug-abusing offenders under community supervision. Federal Probation, 59(4), 66–75.Google Scholar
  58. Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-driven decision making. Big Data, 1(1), 51–59.Google Scholar
  59. Nucleus Research. (2014). Analytics pays back $13.01 for every dollar spent. Report O204. Retrieved from: http://www.gocfi.com/libraries/media/YTL03071USEN.pdf. Accessed 9 Dec 2015.
  60. Roll, J. M., Prendergast, M., Richardson, K., Burdon, W., & Ramirez, A. (2005). Identifying predictors of treatment outcome in a drug court program. The American Journal of Drug and Alcohol Abuse, 31(4), 641–656.Google Scholar
  61. Rossi, P. H., Berk, R. A., & Lenihan, K. J. (1980). Money, work and crime: Some experimental results. New York: Academic.Google Scholar
  62. Saum, C. A., Scarpitti, F. R., & Robbins, C. A. (2001). Violent offenders in drug court. Journal of Drug Issues, 31(1), 107–128.Google Scholar
  63. Saum, C. A., Hiller, M. L., & Nolan, B. A. (2013). Predictors of completion of a driving under the influence (DUI) court for repeat offenders. Criminal Justice Review, 38(2), 207–225.Google Scholar
  64. Schiff, M., & Terry, W. C., III. (1997). Predicting graduation from Broward County’s dedicated drug treatment court. Justice System Journal, 19(3), 291–310.Google Scholar
  65. Schubert, S., Haller, S., Lee, T. (2012). It’s About Time: Discrete time survival analysis using SAS® Enterprise Miner™. SAS Global Forum, Orlando, FL, Paper 132.Google Scholar
  66. Senjo, S. R., & Leip, L. A. (2001). Testing and developing theory in drug court: A four-part logit model to predict program completion. Criminal Justice Policy Review, 12(1), 66–87.Google Scholar
  67. Shaffer, D. K., Hartman, J. L., Listwan, S. J., Howell, T., & Latessa, E. J. (2011). Outcomes among drug court participants: Does drug of choice matter? International Journal of Offender Therapy and Comparative Criminology, 55(1), 155–174.Google Scholar
  68. Shannon, L. M., Jackson Jones, A., Newell, J., & Neal, C. (2018). Examining the impact of prior criminal justice history on 2-year recidivism rates: A comparison of drug court participants and program referrals. International Journal of Offender Therapy and Comparative Criminology, 62(2), 291–312.Google Scholar
  69. Shmueli, G., & Koppius, O. R. (2011). Predictive analytics in information systems research. MIS Quarterly, 35(3), 553–572.Google Scholar
  70. Sniezek, J. A., & Buckley, T. (1995). Cueing and cognitive conflict in judge-advisor decision making. Organizational Behavior and Human Decision Processes, 62(2), 159–174.Google Scholar
  71. Spivak, A. L., & Damphousse, K. R. (2006). Who returns to prison? A survival analysis of recidivism among adult offenders released in Oklahoma, 1985–2004. Justice Research and Policy, 8(2), 57–88.Google Scholar
  72. The National Center on Addiction and Substance Abuse at Columbia University (2010). Behind bars II: Substance abuse and America’s prison population. Google Scholar
  73. Ting, M. H., Chu, C. M., Zeng, G., Li, D., & Chng, G. S. (2018). Predicting recidivism among youth offenders: Augmenting professional judgement with machine learning algorithms. Journal of Social Work, 18(6), 631–649.Google Scholar
  74. Turban, E., & Watkins, P. R. (1986). Integrating expert systems and decision support systems. MIS Quarterly, 10, 121–136.Google Scholar
  75. Verhaaff, A., & Scott, H. (2015). Individual factors predicting mental health court diversion outcome. Research on Social Work Practice, 25(2), 213–228.Google Scholar
  76. Wang, B., & Japkowicz, N. (2004). Imbalanced data set learning with synthetic samples. In Proceedings of IRIS machine learning workshop (p. 19).Google Scholar
  77. Wilson, J. L., Bandyopadhyay, S., Yang, H., Cerulli, C., & Morse, D. S. (2018). Identifying predictors of substance use and recidivism outcome trajectories among drug treatment court clients. Criminal Justice and Behavior, 45(4), 447–467.Google Scholar
  78. Wu, L. J., Altshuler, S. J., Short, R. A., & Roll, J. M. (2012). Predicting drug court outcome among amphetamine-using participants. Journal of Substance Abuse Treatment, 42(4), 373–382.Google Scholar
  79. WYSAC. (2008). Recidivism survival analysis of the serious and violent offender reentry initiative 2003–2007, by M. McLean & S. Butler. (WYSAC technical report no. CJR-801). Laramie, WY: Wyoming Survey & Analysis Center, University of Wyoming.Google Scholar
  80. Yalin-Mor, K. (2011). Using decision support Systems in Judicial Decision making. Retrieved from https://law.tau.ac.il/Heb/_Uploads/dbsAttachedFiles/research_proposal-Keren_Yalin-Mor.pdf. Accessed 9 Dec 2015.
  81. Yeres, S., Gurnell, B., Holmberg, M. (2005). Making sense of incentives and sanctions in working with the substance abuse offender. National Council of Juvenile and Family Court Judges. Retrieved from: http://www.ncjfcj.org/sites/default/files/incentivesandsanctions_july_2009%282%29_0.pdf. 9 Dec 2015.
  82. Zettler, H. R. (2018). Exploring the relationship between dual diagnosis and recidivism in drug court participants. Crime & Delinquency, 64(3), 363–397.Google Scholar
  83. Zgoba, K. M., & Salerno, L. M. (2017). A three-year recidivism analysis of state correctional releases. Criminal Justice Studies, 30(4), 331–345.Google Scholar
  84. Zhou, M. J., Lu, B., Fan, W. P., & Wang, G. A. (2016). Project description and crowdfunding success: An exploratory study. Information Systems Frontiers, 1–16.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Hamed M. Zolbanin
    • 1
    Email author
  • Dursun Delen
    • 2
  • Durand Crosby
    • 3
  • David Wright
    • 3
  1. 1.Department of MIS, Operations Management, and Decision SciencesUniversity of DaytonDaytonUSA
  2. 2.Spears School of BusinessOklahoma State UniversityStillwaterUSA
  3. 3.Department of Mental Health and Substance Abuse ServicesOklahoma CityUSA

Personalised recommendations