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Improved Prediction of Knee Osteoarthritis by the Machine Learning Model XGBoost

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Abstract

Objectives

The accurate prediction of osteoarthritis (OA) severity in patients can be helpful to make the proper decision of intervention. This study aims to build up a powerful model to assess predictive risk factors and severity of knee osteoarthritis (KOA) in the clinical scenario.

Methods

A total of 4796 KOA cases and 1205 features were selected by feature selections from the public OA database, Osteoarthritis Initiative (OAI). Six machine learning-based models were constructed and compared for the accuracy of OA prediction. The gradient-boosting decision tree was used to identify important prediction features in the extreme gradient boosting (XGBoost) model. The performance of models was evaluated by F1-score.

Results

Twenty features were determined as predictors for KOA risk and severity, including the subject characteristics, knee symptoms/risk factors and physical exam. The XGBoost model demonstrated 100% prediction accuracy for 54.7% of examined samples, and the remaining 45.3% of samples showed Kellgren and Lawrence (KL) gradings very close to the actual levels. It showed the highest prediction accuracy with an F1-score of 0.553 among the tested six models.

Conclusions

We demonstrate that the XGBoost is the best model for the prediction of KOA severity in the six examined models. In addition, 20 risk features were determined as the essential predictors of KOA, including the physical exam, knee symptoms/risk factors and subject characteristics, which may be useful for the identification of high-risk KOA cases and for making appropriate treatment decisions as well.

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Data availability

Data are from the Osteoarthritis Initiative (OAI) database, which is available upon request at https://nda.nih.gov/oai/.

References

  1. Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990–2013: A systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2015;386(9995):743–800. doi: https://doi.org/10.1016/s0140-6736(15)60692-4.

  2. McGuire, D. A., Carter, T. R., & Shelton, W. R. (2002). Complex knee reconstruction: Osteotomies, ligament reconstruction, transplants, and cartilage treatment options. Arthroscopy, 18(9 Suppl 2), 90–103. https://doi.org/10.1053/jars.2002.36511

    Article  PubMed  Google Scholar 

  3. Peat, G., McCarney, R., & Croft, P. (2001). Knee pain and osteoarthritis in older adults: A review of community burden and current use of primary health care. Annals of the Rheumatic Diseases, 60(2), 91–97. https://doi.org/10.1136/ard.60.2.91

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Blagojevic, M., Jinks, C., Jeffery, A., & Jordan, K. P. (2010). Risk factors for onset of osteoarthritis of the knee in older adults: A systematic review and meta-analysis. Osteoarthritis Cartilage, 18(1), 24–33. https://doi.org/10.1016/j.joca.2009.08.010

    Article  CAS  PubMed  Google Scholar 

  5. Chan, L. C., Li, H. H. T., Chan, P. K., & Wen, C. (2021). A machine learning-based approach to decipher multi-etiology of knee osteoarthritis onset and deterioration. Osteoarthritis and Cartilage Open, 3(1), 100135. https://doi.org/10.1016/j.ocarto.2020.100135

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Karsdal, M. A., Michaelis, M., Ladel, C., Siebuhr, A. S., Bihlet, A. R., Andersen, J. R., et al. (2016). Disease-modifying treatments for osteoarthritis (DMOADs) of the knee and hip: Lessons learned from failures and opportunities for the future. Osteoarthritis Cartilage, 24(12), 2013–2021. https://doi.org/10.1016/j.joca.2016.07.017

    Article  CAS  PubMed  Google Scholar 

  7. Park, H. J., Kim, S. S., Lee, S. Y., Park, N. H., Park, J. Y., Choi, Y. J., et al. (2013). A practical MRI grading system for osteoarthritis of the knee: Association with Kellgren-Lawrence radiographic scores. European Journal of Radiology, 82(1), 112–117. https://doi.org/10.1016/j.ejrad.2012.02.023

    Article  PubMed  Google Scholar 

  8. Wellner, B., Grand, J., Canzone, E., Coarr, M., Brady, P. W., Simmons, J., et al. (2017). Predicting unplanned transfers to the intensive care unit: A machine learning approach leveraging diverse clinical elements. JMIR Med Informatics, 5(4), e45. https://doi.org/10.2196/medinform.8680

    Article  Google Scholar 

  9. Lazzarini, N., Runhaar, J., Bay-Jensen, A. C., Thudium, C. S., Bierma-Zeinstra, S. M. A., Henrotin, Y., et al. (2017). A machine learning approach for the identification of new biomarkers for knee osteoarthritis development in overweight and obese women. Osteoarthritis Cartilage, 25(12), 2014–2021. https://doi.org/10.1016/j.joca.2017.09.001

    Article  CAS  PubMed  Google Scholar 

  10. Jamshidi, A., Pelletier, J. P., & Martel-Pelletier, J. (2019). Machine-learning-based patient-specific prediction models for knee osteoarthritis. Nature Reviews Rheumatology, 15(1), 49–60. https://doi.org/10.1038/s41584-018-0130-5

    Article  PubMed  Google Scholar 

  11. Faschingbauer, M., Kasparek, M., Waldstein, W., Schadler, P., Reichel, H., & Boettner, F. (2020). Cartilage survival of the knee strongly depends on malalignment: A survival analysis from the Osteoarthritis Initiative (OAI). Knee Surgery, Sports Traumatology, Arthroscopy, 28(5), 1346–1355. https://doi.org/10.1007/s00167-019-05434-1

    Article  PubMed  Google Scholar 

  12. Huang, Z., Guo, W., & Martin, J. T. (2021). Unsupervised and supervised machine learning for establishing back pain phenotypes: Data from the OAI. Osteoarthritis and Cartilage., 29, S300–S301. https://doi.org/10.1016/j.joca.2021.02.394

    Article  Google Scholar 

  13. Eckstein, F., Hudelmaier, M., Wirth, W., Kiefer, B., Jackson, R., Yu, J., et al. (2006). Double echo steady state magnetic resonance imaging of knee articular cartilage at 3 Tesla: A pilot study for the Osteoarthritis Initiative. Annals of the Rheumatic Diseases, 65(4), 433–441. https://doi.org/10.1136/ard.2005.039370

    Article  CAS  PubMed  Google Scholar 

  14. Eckstein, F., Wirth, W., & Nevitt, M. C. (2012). Recent advances in osteoarthritis imaging–the osteoarthritis initiative. Nature Reviews Rheumatology, 8(10), 622–630. https://doi.org/10.1038/nrrheum.2012.113

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Bany Muhammad, M., & Yeasin, M. (2021). Interpretable and parameter optimized ensemble model for knee osteoarthritis assessment using radiographs. Scientific Reports, 11(1), 14348. https://doi.org/10.1038/s41598-021-93851-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Liu, L., Yu, Y., Fei, Z., Li, M., Wu, F.-X., Li, H.-D., et al. (2018). An interpretable boosting model to predict side effects of analgesics for osteoarthritis. BMC Systems Biology, 12(Suppl 6), 105. https://doi.org/10.1186/s12918-018-0624-4

    Article  PubMed  PubMed Central  Google Scholar 

  17. Wang, Q. Q., Yu, S. C., Qi, X., Hu, Y. H., Zheng, W. J., Shi, J. X., et al. (2019). Overview of logistic regression model analysis and application. Zhonghua Yu Fang Yi Xue Za Zhi, 53(9), 955–960. https://doi.org/10.3760/cma.j.issn.0253-9624.2019.09.018

    Article  CAS  PubMed  Google Scholar 

  18. Huang, S., Cai, N., Pacheco, P. P., Narrandes, S., Wang, Y., & Xu, W. (2018). Applications of support vector machine (SVM) learning in cancer genomics. Cancer Genomics & Proteomics, 15(1), 41–51. https://doi.org/10.21873/cgp.20063

    Article  CAS  Google Scholar 

  19. Hand, D. J., & Yu, K. (2001). Idiot’s bayes: Not so stupid after all? International Statistical Review, 69(3), 385–398. https://doi.org/10.2307/1403452

    Article  Google Scholar 

  20. Du, Y., Almajalid, R., Shan, J., & Zhang, M. (2018). A novel method to predict knee osteoarthritis progression on MRI using machine learning methods. IEEE Transactions on Nanobioscience, 17(3), 228–236. https://doi.org/10.1109/tnb.2018.2840082

    Article  PubMed  Google Scholar 

  21. Chen, T., & Guestrin, C. (Ed.). (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining.

  22. Gupta, J., Patrick, J., & Poon, S. (2019). Clinical safety incident taxonomy performance on C4.5 decision tree and random forest. Studies in Health Technology and Informatics, 266, 83–88. https://doi.org/10.3233/shti190777

    Article  PubMed  Google Scholar 

  23. Ntakolia, C., Kokkotis, C., Moustakidis, S., & Tsaopoulos, D. (2021). Prediction of joint space narrowing progression in knee osteoarthritis patients. Diagnostics (Basel). https://doi.org/10.3390/diagnostics11020285

    Article  PubMed  Google Scholar 

  24. Kokkotis, C., Moustakidis, S., Giakas, G., & Tsaopoulos, D. (2020). Identification of risk factors and machine learning-based prediction models for knee osteoarthritis patients. Applied Sciences, 10(19), 6797.

    Article  CAS  Google Scholar 

  25. Weir, C., & Silk, B. (1992). Paramax systems corporation: MUC-4 test results and analysis. In Proceedings of the 4th conference on message understanding (pp. 128–131). McLean, VA: Association for Computational Linguistics.

  26. Tseng, P. Y., Chen, Y. T., Wang, C. H., Chiu, K. M., Peng, Y. S., Hsu, S. P., et al. (2020). Prediction of the development of acute kidney injury following cardiac surgery by machine learning. Critical Care, 24(1), 478. https://doi.org/10.1186/s13054-020-03179-9

    Article  PubMed  PubMed Central  Google Scholar 

  27. Kohn, M. D., Sassoon, A. A., & Fernando, N. D. (2016). Classifications in brief: Kellgren–Lawrence classification of osteoarthritis. Clinical Orthopaedics and Related Research, 474(8), 1886–1893. https://doi.org/10.1007/s11999-016-4732-4

    Article  PubMed  PubMed Central  Google Scholar 

  28. Lane, N. E., Brandt, K., Hawker, G., Peeva, E., Schreyer, E., Tsuji, W., et al. (2011). OARSI-FDA initiative: Defining the disease state of osteoarthritis. Osteoarthritis Cartilage, 19(5), 478–482. https://doi.org/10.1016/j.joca.2010.09.013

    Article  CAS  PubMed  Google Scholar 

  29. Sundhedsstyrelsen. NKR og faglige visitationsretningslinjer: Knæartrose - ikke gældende 2012 [cited 2012 08 NOV]. https://www.sst.dk/da/Udgivelser/2012/NKR-Knaeartrose

  30. Altman, R. D., & Gold, G. E. (2007). Atlas of individual radiographic features in osteoarthritis, revised. Osteoarthritis Cartilage, 15(Suppl A), A1-56. https://doi.org/10.1016/j.joca.2006.11.009

    Article  PubMed  Google Scholar 

  31. Chaisson, C. E., Gale, D. R., Gale, E., Kazis, L., Skinner, K., & Felson, D. T. (2000). Detecting radiographic knee osteoarthritis: What combination of views is optimal? Rheumatology (Oxford), 39(11), 1218–1221. https://doi.org/10.1093/rheumatology/39.11.1218

    Article  CAS  PubMed  Google Scholar 

  32. Pongsakonpruttikul, N., Angthong, C., Kittichai, V., Chuwongin, S., Puengpipattrakul, P., Thongpat, P., et al. (2022). Artificial intelligence assistance in radiographic detection and classification of knee osteoarthritis and its severity: A cross-sectional diagnostic study. European Review for Medical and Pharmacological Sciences, 26(5), 1549–1558. https://doi.org/10.26355/eurrev_202203_28220

    Article  CAS  PubMed  Google Scholar 

  33. Bonakdari, H., Jamshidi, A., Pelletier, J. P., Abram, F., Tardif, G., & Martel-Pelletier, J. (2021). A warning machine learning algorithm for early knee osteoarthritis structural progressor patient screening. Therapeutic Advances in Musculoskeletal Disease. https://doi.org/10.1177/1759720x21993254

    Article  PubMed  PubMed Central  Google Scholar 

  34. Christodoulou, E., Moustakidis, S. P., Papandrianos, N. I., Tsaopoulos, D., & Papageorgiou, E. I. (2019). Exploring deep learning capabilities in knee osteoarthritis case study for classification. In 2019 10th international conference on information, intelligence, systems and applications (IISA) (pp. 1–6).

  35. Kwon, S. B., Han, H. S., Lee, M. C., Kim, H. C., Ku, Y., & Ro, D. H. (2020). Machine learning-based automatic classification of knee osteoarthritis severity using gait data and radiographic images. IEEE Access, 8, 120597–120603. https://doi.org/10.1109/ACCESS.2020.3006335

    Article  Google Scholar 

  36. Kokkotis, C., Ntakolia, C., Moustakidis, S., Giakas, G., & Tsaopoulos, D. (2022). Explainable machine learning for knee osteoarthritis diagnosis based on a novel fuzzy feature selection methodology. Physical and Engineering Sciences in Medicine, 45(1), 219–229. https://doi.org/10.1007/s13246-022-01106-6

    Article  PubMed  PubMed Central  Google Scholar 

  37. Almhdie-Imjabbar, A., Nguyen, K. L., Toumi, H., Jennane, R., & Lespessailles, E. (2022). Prediction of knee osteoarthritis progression using radiological descriptors obtained from bone texture analysis and Siamese neural networks: Data from OAI and MOST cohorts. Arthritis Research & Therapy, 24(1), 66. https://doi.org/10.1186/s13075-022-02743-8

    Article  CAS  Google Scholar 

  38. Khalid, A., Senan, E. M., Al-Wagih, K., Ali Al-Azzam, M. M., & Alkhraisha, Z. M. (2023). Hybrid techniques of X-ray analysis to predict knee osteoarthritis grades based on fusion features of CNN and handcrafted. Diagnostics (Basel)., 13(9), 1609. https://doi.org/10.3390/diagnostics13091609

    Article  PubMed  PubMed Central  Google Scholar 

  39. Kim, B. Y., Kim, H. A., Jung, J. Y., Choi, S. T., Kim, J. M., Kim, S. H., et al. (2019). Clinical impact of the fracture risk assessment tool on the treatment decision for osteoporosis in patients with knee osteoarthritis: A multicenter comparative study of the fracture risk assessment tool and world health organization criteria. Journal of Clinical Medicine, 8(7), 918. https://doi.org/10.3390/jcm8070918

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

Data used in the preparation of this manuscript were obtained and analyzed from the controlled access data sets distributed from the Osteoarthritis Initiative (OAI), a data repository housed within the NIMH Data Archive (NDA). OAI is a collaborative informatics system created by the National Institute of Mental Health and the National Institute of Arthritis, Musculoskeletal and Skin Diseases (NIAMS) to provide a worldwide resource to quicken the pace of biomarker identification, scientific investigation and OA drug development. Data set identifier: NIMH Data Archive Digital Object Identifier (DOI): https://doi.org/10.15154/1528318.

Funding

This work has been supported by the Guangdong Provincial Talented Scholar Foundation (Grant Number 220418137), the National Natural Science Foundation of China (Grant Number 82173850) and University Innovative Team Support for Major Chronic Diseases and Drug Development (Grant Number 26330320901).

Author information

Author notes

  1. Kui Su, Xin Yuan, and Yukai Huang contributed equally to this study.

Authors and Affiliations

  1. School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, Higher Education Mega Center, 100 Outside Ring West Road, Guangzhou, 510006, People’s Republic of China

    Kui Su, Xin Yuan, Qian Yuan, Minghui Yang, Jianwu Sun, Shuyi Li, Xinyi Long, Lang Liu & Zhengqiang Yuan

  2. Department of Rheumatology and Immunology, Guangdong Second Provincial General Hospital, Guangzhou, 510317, People’s Republic of China

    Yukai Huang & Tianwang Li

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  1. Kui Su
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Correspondence to Tianwang Li or Zhengqiang Yuan.

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The authors have no relevant competing interests to disclose.

Ethical standard statement

A permission to access data in the Osteoarthritis Initiative permission group in the NIMH Data Archive (NDA) was applied for and has been approved, which is valid for a period of 1 year until 12/18/2022. No identifiable information of the participants is included in either the data or the manuscript. NDA data have been permanently deleted from all machines after the research was completed.

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Su, K., Yuan, X., Huang, Y. et al. Improved Prediction of Knee Osteoarthritis by the Machine Learning Model XGBoost. JOIO 57, 1667–1677 (2023). https://doi.org/10.1007/s43465-023-00936-0

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