Skip to main content
SpringerLink
Log in

Reliable prediction of anti-diabetic drug failure using a reject option

  • Industrial and Commercial Application
  • Published:
Pattern Analysis and Applications Aims and scope Submit manuscript

Abstract

The medical care for patients with type 2 diabetes generally involves ingestion of oral hypoglycemic agents in order to lower their glucose level. When predicting the result of the medication using a classification approach, high prediction accuracy of the classifier is essential because of high misclassification costs. The application of a reject option to this approach supports more accurate prediction, allowing for human experts to examine when the classifier is unreliable to predict. In this paper, we propose a reject option framework based on heterogeneous ensemble learning through a two-phase fusion. The first phase is to calculate confidence scores, which are used to determine whether to predict, and the second phase is to derive final prediction results by fusing the outputs from multiple heterogeneous classifiers. We confirm the effectiveness of the proposed method to the anti-diabetic drug failure prediction problem through experiments on actual electronic medical records data of type 2 diabetes. The proposed method yields a better trade-off between accuracy and rejection than other reject options with statistical significance. A lower prediction error is obtained for the same degree of rejection. We obtained desirable accuracy for the anti-diabetic drug failure problem by applying the proposed reject option, which allows using the classification approach in practice. The accurate prediction of drug failure at the moment of prescription can assist clinical decisions for patients. In addition, in-depth analysis can be considered for those prescriptions that are predicted as failure or rejected.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. American Diabetes Association (2014) Standards of medical care in diabetes—2014. Diabetes Care 37(Supplement 1):S14–S80. doi:10.2337/dc14-S014

  2. Barakat N, Bradley AP, Barakat MNH (2010) Intelligible support vector machines for diagnosis of diabetes mellitus. IEEE Trans Inf Technol Biomed 14(4):1114–1120

    Article  Google Scholar 

  3. Bennett CM, Guo M, Dharmage SC (2007) HbA1c as a screening tool for detection of type 2 diabetes: a systematic review. Diabetic Med 24(4):333–343. doi:10.1111/j.1464-5491.2007.02106.x

    Article  Google Scholar 

  4. Cecotti H, Vajda S (2013) Rejection schemes in multi-class classification—application to handwritten character recognition. In: Proceedings of the 12th international conference on document analysis and recognition, pp 445–449. doi:10.1109/ICDAR.2013.96

  5. Cho SB, Kim JH (1995) Multiple network fusion using fuzzy logic. IEEE Trans Neural Netw 6(2):497–501. doi:10.1109/72.363487

    Article  Google Scholar 

  6. Chow C (1970) On optimum recognition error and reject tradeoff. IEEE Trans Inf Theory 16(1):41–46. doi:10.1109/TIT.1970.1054406

    Article  MathSciNet  MATH  Google Scholar 

  7. Duan KB, Keerthi SS (2005) Which is the best multiclass SVM method? An empirical study. In: Oza NC, Polikar R, Kittler J, Roli F (eds) Multiple classifier systems. Lecture notes in computer science, vol 3541. Springer, Berlin, pp 278–285

    Chapter  Google Scholar 

  8. Džeroski S, Ženko B (2004) Is combining classifiers with stacking better than selecting the best one? Mach Learn 54(3):255–273. doi:10.1023/B:MACH.0000015881.36452.6e

    Article  MATH  Google Scholar 

  9. Fumera G, Roli F (2002) Support vector machines with embedded reject option. In: Lee SW, Verri A (eds) Pattern recognition with support vector machines. Lecture notes in computer science, vol 2388. Springer, Berlin, pp 68–82

    Chapter  Google Scholar 

  10. Galar M, Fernández A, Barrenechea E, Bustince H, Herrera F (2011) An overview of ensemble methods for binary classifiers in multi-class problems: experimental study on one-vs-one and one-vs-all schemes. Pattern Recognit 44(8):1761–1776. doi:10.1016/j.patcog.2011.01.017

    Article  Google Scholar 

  11. Grandvalet Y, Rakotomamonjy A, Keshet J, Canu S (2008) Support vector machines with a reject option. In: Koller D, Schuurmans D, Bengio Y, Bottou L (eds) Advances in neural information processing systems, vol 21. Curran & Associates Inc., New York, pp 537–544

  12. Han L, Luo S, Yu J, Pan L, Chen S (2015) Rule extraction from support vector machines using ensemble learning approach: an application for diagnosis of diabetes. IEEE J Biomed Health Inform 19(2):728–734. doi:10.1109/JBHI.2014.2325615

    Article  Google Scholar 

  13. Herbei R, Wegkamp MH (2006) Classification with reject option. Can J Stat 34(4):709–721. doi:10.1002/cjs.5550340410

    Article  MathSciNet  MATH  Google Scholar 

  14. Hsu CW, Chang CC, Lin CJ (2003) A practical guide to support vector classification. Tech. rep., Department of Computer Science, National Taiwan University

  15. Hsu CW, Lin CJ (2002) A comparison of methods for multiclass support vector machines. IEEE Trans Neural Netw 13(2):415–425. doi:10.1109/72.991427

    Article  Google Scholar 

  16. Huang Y, McCullagh P, Black N, Harper R (2007) Feature selection and classification model construction on type 2 diabetic patients data. Artif Intell Med 41(3):251–262. doi:10.1016/j.artmed.2007.07.002

    Article  Google Scholar 

  17. Kang S, Cho S (2015) A novel multi-class classification algorithm based on one-class support vector machine. Intell Data Anal 19(4):713–725. doi:10.3233/IDA-150741

    Article  Google Scholar 

  18. Kang S, Cho S (2015) Optimal construction of one-against-one classifier based on meta-learning. Neurocomputing. doi:10.1016/j.neucom.2015.04.048

    Google Scholar 

  19. Kang S, Cho S, Kang P (2015) Constructing a multi-class classifier using one-against-one approach with different binary classifiers. Neurocomputing 149:677–682. doi:10.1016/j.neucom.2014.08.006

    Article  Google Scholar 

  20. Kang S, Cho S, Kang P (2015) Multi-class classification via heterogeneous ensemble of one-class classifiers. Eng Appl Artif Intell 43:35–43. doi:10.1016/j.engappai.2015.04.003

    Article  Google Scholar 

  21. Kang S, Kang P, Ko T, Cho S, Rhee SJ, Yu KS (2015) An efficient and effective ensemble of support vector machines for anti-diabetic drug failure prediction. Expert Syst Appl 42(9):4265–4273. doi:10.1016/j.eswa.2015.01.042

    Article  Google Scholar 

  22. Kaur H, Wasan SK (2006) Empirical study on applications of data mining techniques in healthcare. J Comput. Sci 2(2):194. doi:10.3844/jcssp.2006.194.200

    Article  Google Scholar 

  23. Koh HC, Tan G (2005) Data mining applications in healthcare. J Healthc Inf Manag 19(2):64–72

    Google Scholar 

  24. Kuncheva LI (2001) Using measures of similarity and inclusion for multiple classifier fusion by decision templates. Fuzzy Sets Syst 122(3):401–407. doi:10.1016/S0165-0114(99)00161-X

    Article  MathSciNet  MATH  Google Scholar 

  25. Kuncheva LI, Bezdek JC, Duin RP (2001) Decision templates for multiple classifier fusion: an experimental comparison. Pattern Recognit. 34(2):299–314. doi:10.1016/S0031-3203(99)00223-X

    Article  MATH  Google Scholar 

  26. Landgrebe TCW, Tax DMJ, Paclík P, Duin RPW (2006) The interaction between classification and reject performance for distance-based reject-option classifiers. Pattern Recognit Lett 27(8):908–917. doi:10.1016/j.patrec.2005.10.015

    Article  Google Scholar 

  27. Lu ZX, Walker KZ, O’Dea K, Sikaris KA, Shaw JE (2010) A1C for screening and diagnosis of type 2 diabetes in routine clinical practice. Diabetes Care 33(4):817–819. doi:10.2337/dc09-1763

    Article  Google Scholar 

  28. Lughofer E, Buchtala O (2013) Reliable all-pairs evolving fuzzy classifiers. IEEE Trans Fuzzy Syst 21(4):625–641. doi:10.1109/TFUZZ.2012.2226892

    Article  Google Scholar 

  29. Lughofer E, Weigl E, Heidl W, Eitzinger C, Radauer T (2015) Integrating new classes on the fly in evolving fuzzy classifier designs and their application in visual inspection. Appl Soft Comput 35:558–582. doi:10.1016/j.asoc.2015.06.038

    Article  Google Scholar 

  30. Mani S, Chen Y, Elasy T, Clayton W, Denny J (2012) Type 2 diabetes risk forecasting from EMR data using machine learning. In: AMIA annual symposium proceedings, vol 2012. American Medical Informatics Association, pp 606–615

  31. Marinov M, Mosa ASM, Yoo I, Boren SA (2011) Data-mining technologies for diabetes: a systematic review. J Diabetes Sci Technol 5(6):1549–1556. doi:10.1177/193229681100500631

    Article  Google Scholar 

  32. Nadeem MSA, Zucker JD, Hanczar B (2010) Accuracy-rejection curves (ARCs) for comparing classification methods with a reject option. In: Machine learning in systems biology, journal of machine learning research workshop and conference proceedings vol 8, pp 65–81

  33. Oshiro TM, Perez PS, Baranauskas JA (2012) How many trees in a random forest? In: Proceedings of the 8th international conference on machine learning and data mining, pp 154–168

  34. Rogova G (1994) Combining the results of several neural network classifiers. Neural Netw 7(5):777–781. doi:10.1016/0893-6080(94)90099-X

    Article  Google Scholar 

  35. Rokach L (2010) Ensemble-based classifiers. Artif Intell Rev 33(1–2):1–39. doi:10.1007/s10462-009-9124-7

    Article  Google Scholar 

  36. Sannen D, Lughofer E, Van Brussel H (2010) Towards incremental classifier fusion. Intell Data Anal 14(1):3–30. doi:10.3233/IDA-2010-0406

    Google Scholar 

  37. Simeone P, Marrocco C, Tortorella F (2012) Design of reject rules for ECOC classification systems. Pattern Recognit 45(2):863–875. doi:10.1016/j.patcog.2011.08.001

    Google Scholar 

  38. Tax DMJ, Duin RPW (2008) Growing a multi-class classifier with a reject option. Pattern Recognit Lett 29(10):1565–1570. doi:10.1016/j.patrec.2008.03.010

    Article  Google Scholar 

  39. Temurtas H, Yumusak N, Temurtas F (2009) A comparative study on diabetes disease diagnosis using neural networks. Expert Syst Appl 36(4):8610–8615. doi:10.1016/j.eswa.2008.10.032

    Article  Google Scholar 

  40. Ting KM, Witten IH (1999) Issues in stacked generalization. J Artif Intell Res 10:271–289. doi:10.1613/jair.594

    MATH  Google Scholar 

  41. Tortorella F (2005) A ROC-based reject rule for dichotomizers. Pattern Recognit Lett 26(2):167–180. doi:10.1016/j.patrec.2004.09.004

    Article  MathSciNet  Google Scholar 

  42. Varshney KR (2011) A risk bound for ensemble classification with a reject option. In: Proceedings of the 2011 IEEE statistical signal processing workshop, pp 769–772. doi:10.1109/SSP.2011.5967817

Download references

Acknowledegements

This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea Government (MSIP) (No. 2011-0030814).

Author information

Authors and Affiliations

  1. Department of Industrial Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea

    Seokho Kang & Sungzoon Cho

  2. Department of Clinical Pharmacology and Therapeutics, Seoul National University College of Medicine and Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea

    Su-jin Rhee & Kyung-Sang Yu

Authors
  1. Seokho Kang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sungzoon Cho
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Su-jin Rhee
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kyung-Sang Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sungzoon Cho.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kang, S., Cho, S., Rhee, Sj. et al. Reliable prediction of anti-diabetic drug failure using a reject option. Pattern Anal Applic 20, 883–891 (2017). https://doi.org/10.1007/s10044-016-0585-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10044-016-0585-4

Keywords

Search

Navigation