Skip to main content
SpringerLink
Log in

Synthetic Data Generation System for AI-Based Diabetic Foot Diagnosis

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

The paucity of readily available medical data poses a major challenge for the development of AI (artificial intelligence)-based healthcare applications and devices. To aid in overcoming this challenge, we propose a sensor-based medical time series data synthesis system especially designed for the training of diabetic foot diagnosis models. The proposed system utilizes statistical methods, augmentation techniques, and the NeuralProphet model to accomplish its purpose while still maintaining medical validity. Our results show that the generated synthetic time series data follow the trends and tendencies of real data. We also verify our work using machine learning-based clustering. By successfully clustering the synthetic data generated by our proposed system, we prove that our system is capable of meeting its objectives.

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
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. “Diabetes”. World Health Organization; 2020. https://www.who.int/news-room/fact-sheets/detail/diabetes. Accessed 1 Apr 2021.

  2. American Podiatric Medical Association. Diabetic wound care.

  3. Nobuyoshi A. The diagnostic classification of critical limb ischemia. Ann Vasc Dis. 2018;ra-18.

  4. Kalani M, Brismar K, Fagrell B, Ostergren J, Jörneskog G. Transcutaneous oxygen tension and toe blood pressure as predictors for outcome of diabetic foot ulcers. Diabetes Care. 1999;22(1):147–51.

    Article  Google Scholar 

  5. de Meijer VE, Van’t Sant HP, Spronk S, Kusters FJ, den Hoed PT. Reference value of transcutaneous oxygen measurement in diabetic patients compared with nondiabetic patients. J Vasc Surg. 2008;48(2):382–8.

    Article  Google Scholar 

  6. Murch C. Tramul: transcutaneous oxygen measurement and diabetic foot ulceration. Diabet Foot J. 2019;22(2):48–54.

    Google Scholar 

  7. Wyss CR, Matsen FA 3rd, Simmons CW, Burgess EM. Transcutaneous oxygen tension measurements on limbs of diabetic and nondiabetic patients with peripheral vascular disease. Surgery. 1984;95(3):339–46.

    Google Scholar 

  8. Yang C, Weng H, Chen L, Yang H, Luo G, Mai L, Jin G, Yan L. Transcutaneous oxygen pressure measurement in diabetic foot ulcers: mean values and cut-point for wound healing. J Wound Ostomy Cont Nurs. 2013;40(6):585–9.

    Article  Google Scholar 

  9. Jayun H, Lee SH, Son HM, Park J-U, Chung T-M. A synthetic data generation model for diabetic foot treatment. In: International conference on future data and security engineering. Springer; 2020. p. 249–64.

  10. Ming LJZ, Ng LNS, Thomas C. Prevention and treatment of diabetic foot ulcers. J R Soc Med. 2017;110(3):104–9.

    Article  Google Scholar 

  11. Pendsey S. Understanding diabetic foot. Int J Diabetes Dev Ctries. 2010;30(2):75.

    Article  Google Scholar 

  12. Singh S, Pai DR, Yuhhui C. Diabetic foot ulcer-diagnosis and management. Clin Res Foot Ankle. 2013;1(3):120.

    Google Scholar 

  13. Yale A, Dash S, Dutta R, Guyon I, Pavao A, Bennett KP. Generation and evaluation of privacy preserving synthetic health data. Neurocomputing. 2020;416:244–55.

    Article  Google Scholar 

  14. Dahmen J, Cook D. Synsys: a synthetic data generation system for healthcare applications. Sensors. 2019;19(5):1181.

    Article  Google Scholar 

  15. Walonoski J, Kramer M, Nichols J, Quina A, Moesel C, Hall D, Duffett C, Dube K, Gallagher T, McLachlan S. Synthea: an approach, method, and software mechanism for generating synthetic patients and the synthetic electronic health care record. J Am Med Inform Assoc. 2018;25(3):230–8.

    Article  Google Scholar 

  16. Esteban C, Hyland SL, Rätsch G. Real-valued (medical) time series generation with recurrent conditional gans (2017). arXiv:1706.02633.

  17. Yale A, Dash S, Dutta R, Guyon I, Pavao A, Bennett KP. Generation and evaluation of privacy preserving synthetic health data. Neurocomputing. 2020;416:244–55.

    Article  Google Scholar 

  18. Mata AG. A comparison between lstm and facebook prophet models: a financial forecasting case study.

  19. Liu W, Qin C, Gao K, Li H, Qin Z, Cao Y, Si W. Research on medical data feature extraction and intelligent recognition technology based on convolutional neural network. IEEE Access. 2019;7:150157–67.

    Article  Google Scholar 

  20. Um TT, Pfister FMJ, Pichler D, Endo S, Lang M, Hirche S, Fietzek U, Kulić D. Data augmentation of wearable sensor data for Parkinson’s disease monitoring using convolutional neural networks. In: Proceedings of the 19th ACM international conference on multimodal interaction. 2017. p. 216–20.

  21. Deng S, Hua W, Wang B, Wang G, Zhou X. Few-shot human activity recognition on noisy wearable sensor data. In: International conference on database systems for advanced applications. Springer; 2020. p. 54–72.

  22. Wen Q, Sun L, Song X, Gao J, Wang X, Xu H. Time series data augmentation for deep learning: a survey. 2020. arXiv:2002.12478.

  23. Triebe O, Laptev N, Rajagopal R. Ar-net: a simple auto-regressive neural network for time-series. 2019. CoRR. arXiv:1911.12436.

  24. Benhamou Y, Begarin L, David N, Cailleux N, Bessin C, Lévesque H, Edet S. Detection of microcirculatory impairment by transcutaneous oxymetry monitoring during hemodialysis: an observational study. BMC Nephrol. 2014;15(1):1–8.

    Article  Google Scholar 

  25. Makris K, Spanou L. Is there a relationship between mean blood glucose and glycated hemoglobin? J Diabetes Sci Technol. 2011;5(6):1572–83.

    Article  Google Scholar 

  26. Elaine Biostatistics. An introduction to medical statistics for health care professionals: describing and presenting data. Musculoskelet Care. 2004;2:218–28.

  27. Dua D, Graff C, et al. Uci machine learning repository. 2017.

  28. National diabetes statistics report. 2020.

  29. Selvin E, Zhu H, Brancati FL. Elevated a1c in adults without a history of diabetes in the U.S. Diabetes Care. 2009;32(5):828–33.

    Article  Google Scholar 

  30. Lund E. Comparison of additive and multiplicative models for reproductive risk factors and post-menopausal breast cancer. Stat Med. 1995;14(3):267–74.

    Article  Google Scholar 

  31. Tian Y, Huffman GJ, Adler RF, Tang L, Sapiano M, Maggioni V, Huan W. Modeling errors in daily precipitation measurements: additive or multiplicative? Geophys Res Lett. 2013;40(10):2060–5.

    Article  Google Scholar 

  32. Yan X, Xie H, Tong W. A multiple linear regression data predicting method using correlation analysis for wireless sensor networks. In: Proceedings of 2011 cross strait quad-regional radio science and wireless technology conference. 2011.

  33. Wang W, Lyu G, Shi Y, Liang X. Time series clustering based on dynamic time warping. In: 2018 IEEE 9th international conference on software engineering and service science (ICSESS). 2018. p. 487–90.

  34. Tavenard R, Faouzi J, Vandewiele G, Divo F, Androz G, Holtz C, Payne M, Yurchak R, Rußwurm M, Kolar K, Woods E. Tslearn, a machine learning toolkit for time series data. J Mach Learn Res. 2020;21(118):1–6.

    MATH  Google Scholar 

Download references

Funding

This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2020-0-00990, Platform Development and Proof of High Trust & Low Latency Processing for Heterogeneous.Atypical.Large Scaled Data in 5G-IoT Environment).

Author information

Author notes

  1. Yongho Lee

    Present address: Internet Management Technology Lab, Sungkyunkwan University, Suwon, Republic of Korea

Authors and Affiliations

  1. Department of Computer Science and Engineering, Sungkyunkwan University, Suwon, Republic of Korea

    Jayun Hyun, Ha Min Son, Vinh Pham & Tai-Myoung Chung

  2. College of International Studies, Kyung Hee University, Yongin, Republic of Korea

    Yongho Lee

  3. Department of Artificial Intelligence, Sungkyunkwan University, Suwon, Republic of Korea

    Seo Hu Lee

  4. Department of Plastic and Reconstructive Surgery, Seoul National University Boramae Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea

    Ji Ung Park

Authors
  1. Jayun Hyun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yongho Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ha Min Son
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Seo Hu Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Vinh Pham
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ji Ung Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Tai-Myoung Chung
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Jayun Hyun or Tai-Myoung Chung.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

In this study, the original research has been carried out by following the ethical principles.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the topical collection “Future Data and Security Engineering 2020” guest edited by Tran Khanh Dang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hyun, J., Lee, Y., Son, H.M. et al. Synthetic Data Generation System for AI-Based Diabetic Foot Diagnosis. SN COMPUT. SCI. 2, 345 (2021). https://doi.org/10.1007/s42979-021-00667-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-021-00667-9

Keywords

Search

Navigation