Skip to main content
SpringerLink
Log in

Comparing Effectiveness of Machine Learning Methods for Diagnosis of Deep Vein Thrombosis

  • Conference paper
  • First Online:
Book cover Computational Science and Its Applications – ICCSA 2022 Workshops (ICCSA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13381))

Included in the following conference series:

Abstract

This paper presents the results of a comparative study of machine learning techniques when predicting deep vein thrombosis. We used the Ri-Schedule dataset with Electronic Health Records of suspected thrombotic patients for training and validation. A total of 1653 samples and 59 predictors were included in this study.

We have compared 20 standard machine learning algorithms and identified the best-performing ones: Random Forest, XGBoost, GradientBoosting and HistGradientBoosting classifiers. After hyper-parameter optimization, the best overall accuracy of 0.91 was shown by GradientBoosting classifier using only 15 of the original variables.

We have also tuned the algorithms for maximum sensitivity. The best specificity was offered by Random Forests. At maximum sensitivity of 1.0 and specificity of 0.41, the Random Forest model was able to identify 23% additional negative cases over the screening practice in use today.

These results suggest that machine learning could offer practical value in real-life implementations if combined with traditional methods for ruling out deep vein thrombosis.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bishop, C.M., et al.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)

    MATH  Google Scholar 

  2. Bordes, A., Bottou, L., Gallinari, P.: SGD-QN: careful quasi-newton stochastic gradient descent. J. Mach. Learn. Res. 10, 1737–1754 (2009)

    MathSciNet  MATH  Google Scholar 

  3. Božič, M., Blinc, A., Stegnar, M.: D-dimer, other markers of haemostasis activation and soluble adhesion molecules in patients with different clinical probabilities of deep vein thrombosis. Thromb. Res. 108(2), 107–114 (2002). https://doi.org/10.1016/S0049-3848(03)00007-0

    Article  Google Scholar 

  4. Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996). https://doi.org/10.1007/BF00058655

    Article  MATH  Google Scholar 

  5. Breiman, L.: Rejoinder: arcing classifiers. Ann. Stat. 26(3), 841–849 (1998). http://www.jstor.org/stable/120059

  6. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001). https://doi.org/10.1023/A:1010933404324

    Article  MATH  Google Scholar 

  7. Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Routledge, New York (2017)

    Book  Google Scholar 

  8. Chan, T., Golub, G., LeVeque, R.: Technical report STAN-CS-79-773, Department of Computer Science (1979)

    Google Scholar 

  9. Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016)

    Google Scholar 

  10. Coleman, D.M., Wakefield, T.W.: Biomarkers for the diagnosis of deep vein thrombosis. Expert Opin. Med. Diagn. 6(4), 253–257 (2012)

    Article  Google Scholar 

  11. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995). https://doi.org/10.1007/BF00994018

    Article  MATH  Google Scholar 

  12. Crammer, K., Dekel, O., Keshet, J., Shalev-Shwartz, S., Singer, Y.: Online passive aggressive algorithms (2006)

    Google Scholar 

  13. Douma, R.A., et al.: Using an age-dependent D-dimer cut-off value increases the number of older patients in whom deep vein thrombosis can be safely excluded. Haematologica 97(10), 1507 (2012)

    Article  Google Scholar 

  14. Fisher, R.A.: The use of multiple measurements in taxonomic problems. Ann. Eugen. 7(2), 179–188 (1936)

    Article  Google Scholar 

  15. Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29(5), 1189–1232 (2001). https://doi.org/10.1214/aos/1013203451

    Article  MathSciNet  MATH  Google Scholar 

  16. Fronas, S.G., et al.: Safety of D-dimer testing as a stand-alone test for the exclusion of deep vein thrombosis as compared with other strategies. J. Thromb. Haemost. 16(12), 2471–2481 (2018). https://doi.org/10.1111/jth.14314

    Article  Google Scholar 

  17. Fronas, S.G., et al.: Safety and feasibility of rivaroxaban in deferred workup of patients with suspected deep vein thrombosis. Blood Adv. 4(11), 2468–2476 (2020). https://doi.org/10.1182/bloodadvances.2020001556

    Article  Google Scholar 

  18. Hoerl, A.E., Kennard, R.W.: Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12(1), 55–67 (1970). https://doi.org/10.1080/00401706.1970.10488634

    Article  MATH  Google Scholar 

  19. Hosmer, D.W., Jr., Lemeshow, S., Sturdivant, R.X.: Applied Logistic Regression, vol. 398. Wiley, Hoboken (2013)

    Book  Google Scholar 

  20. Johnson, E.D., Schell, J.C., Rodgers, G.M.: The D-dimer assay. Am. J. Hematol. 94(7), 833–839 (2019)

    Google Scholar 

  21. Ke, G., et al.: LightGBM: a highly efficient gradient boosting decision tree. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc. (2017). https://proceedings.neurips.cc/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf

  22. Le Gal, G., et al.: Prediction of pulmonary embolism in the emergency department: the revised Geneva score. Ann. Intern. Med. 144(3), 165–171 (2006). https://doi.org/10.7326/0003-4819-144-3-200602070-00004

    Article  Google Scholar 

  23. Lippi, G., Cervellin, G., Franchini, M., Favaloro, E.J.: Biochemical markers for the diagnosis of venous thromboembolism: the past, present and future. J. Thromb. Thrombolysis 30(4), 459–471 (2010). https://doi.org/10.1007/s11239-010-0460-x

    Article  Google Scholar 

  24. Luo, L., Kou, R., Feng, Y., Xiang, J., Zhu, W.: Cost-effective machine learning based clinical pre-test probability strategy for DVT diagnosis in neurological intensive care unit. Clin. Appl. Thromb. Hemost. 27 (2021). https://doi.org/10.1177/10760296211008650

  25. Ma, H., et al.: A novel hierarchical machine learning model for hospital-acquired venous thromboembolism risk assessment among multiple-departments. J. Biomed. Inform. 122, 103892 (2021). https://doi.org/10.1016/j.jbi.2021.103892

    Article  Google Scholar 

  26. Nafee, T., et al.: Machine learning to predict venous thrombosis in acutely ill medical patients. Res. Pract. Thromb. Haemost. 4(2), 230–237 (2020). https://doi.org/10.1002/rth2.12292

    Article  Google Scholar 

  27. Peterson, L.E.: K-nearest neighbor. Scholarpedia 4(2), 1883 (2009)

    Article  Google Scholar 

  28. Ruder, S.: An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747 (2016)

  29. Schapire, R.E.: Explaining AdaBoost. In: Schölkopf, B., Luo, Z., Vovk, V. (eds.) Empirical Inference, pp. 37–52. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41136-6_5

    Chapter  Google Scholar 

  30. Tharwat, A.: Linear vs quadratic discriminant analysis classifier: a tutorial. Int. J. Appl. Pattern Recogn. 3(2), 145–180 (2016)

    Article  Google Scholar 

  31. Wang, K.Y., et al.: Using predictive modeling and supervised machine learning to identify patients at risk for venous thromboembolism following posterior lumbar fusion. Glob. Spine J. (2021). https://doi.org/10.1177/21925682211019361

  32. Wang, X., Yang, Y.Q., Liu, S.H., Hong, X.Y., Sun, X.F., Shi, J.H.: Comparing different venous thromboembolism risk assessment machine learning models in Chinese patients. J. Eval. Clin. Pract. 26(1), 26–34 (2020). https://doi.org/10.1111/jep.13324

    Article  Google Scholar 

  33. Wells, P.S., et al.: Value of assessment of pretest probability of deep-vein thrombosis in clinical management. The Lancet 350(9094), 1795–1798 (1997). https://doi.org/10.1016/S0140-6736(97)08140-3

    Article  Google Scholar 

  34. Wilbur, J., Shian, B.: Diagnosis of deep venous thrombosis and pulmonary embolism. Am. Fam. Physician 86(10), 913–919 (2012)

    Google Scholar 

  35. Williams, C.K., Rasmussen, C.E.: Gaussian Processes for Machine Learning, vol. 2. MIT Press, Cambridge (2006)

    MATH  Google Scholar 

  36. Xue, B., et al.: Use of machine learning to develop and evaluate models using preoperative and intraoperative data to identify risks of postoperative complications. JAMA Netw. Open 4(3), e212240 (2021). https://doi.org/10.1001/jamanetworkopen.2021.2240

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Østfold University College, Halden, Norway

    Ruslan Sorano, Lars V. Magnusson & Khurshid Abbas

Authors
  1. Ruslan Sorano
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lars V. Magnusson
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Khurshid Abbas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ruslan Sorano .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Østfold University College, Halden, Norway

    Sanjay Misra

  4. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  5. University of Cagliari, Cagliari, Italy

    Chiara Garau

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sorano, R., Magnusson, L.V., Abbas, K. (2022). Comparing Effectiveness of Machine Learning Methods for Diagnosis of Deep Vein Thrombosis. In: Gervasi, O., Murgante, B., Misra, S., Rocha, A.M.A.C., Garau, C. (eds) Computational Science and Its Applications – ICCSA 2022 Workshops. ICCSA 2022. Lecture Notes in Computer Science, vol 13381. Springer, Cham. https://doi.org/10.1007/978-3-031-10548-7_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-10548-7_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-10547-0

  • Online ISBN: 978-3-031-10548-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation