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A Prognosis Tool Based on Fuzzy Anthropometric and Questionnaire Data for Obstructive Sleep Apnea Severity

  • Systems-Level Quality Improvement
  • Published:
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Abstract

Obstructive sleep apnea (OSA) are linked to the augmented risk of morbidity and mortality. Although polysomnography is considered a well-established method for diagnosing OSA, it suffers the weakness of time consuming and labor intensive, and requires doctors and attending personnel to conduct an overnight evaluation in sleep laboratories with dedicated systems. This study aims at proposing an efficient diagnosis approach for OSA on the basis of anthropometric and questionnaire data. The proposed approach integrates fuzzy set theory and decision tree to predict OSA patterns. A total of 3343 subjects who were referred for clinical suspicion of OSA (eventually 2869 confirmed with OSA and 474 otherwise) were collected, and then classified by the degree of severity. According to an assessment of experiment results on g-means, our proposed method outperforms other methods such as linear regression, decision tree, back propagation neural network, support vector machine, and learning vector quantization. The proposed method is highly viable and capable of detecting the severity of OSA. It can assist doctors in pre-diagnosis of OSA before running the formal PSG test, thereby enabling the more effective use of medical resources.

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References

  1. Young, T., Palta, M., Dempsey, J., Skatrud, J., Weber, S., and Badr, S., The occurrence of sleep-disordered breathing among middle-aged adults. N. Engl. J. Med. 328(17):1230–1235, 1993.

    Article  CAS  PubMed  Google Scholar 

  2. Wright, J., Johns, R., Watt, I., Melville, A., and Sheldon, T., Health effects of obstructive sleep apnea and the effectiveness of continuous positive airway pressure: a systematic review of the research evidence. Br. Med. J. 314:851–860, 1997.

    Article  CAS  Google Scholar 

  3. Tan, A. C., and Gilbert, D., Ensemble machine learning on gene expression data for cancer classification. Appl. Bioinform. 2:S75–83, 2003.

    CAS  Google Scholar 

  4. Chattopadhyay, S., and Acharya, U. R., A novel mathematical approach to diagnose premenstrual syndrome. J. Med. Syst. 36(4):2177–2186, 2012.

    Article  PubMed  Google Scholar 

  5. Demsar, J., Zupan, B., Aoki, N., Wall, M. J., Granchi, T. H., and Beck, J. R., Feature mining and predictive model construction from severe trauma patient’s data. Int. J. Med. Inform. 63(1–2):41–50, 2001.

    Article  CAS  PubMed  Google Scholar 

  6. Goker, I., Osman, O., Ozekes, S., Baslo, M. B., Ertas, M., and Ulgen, Y., Classification of juvenile myoclonic epilepsy data acquired through scanning electromyography with machine learning algorithms. J. Med. Syst. 36(5):2705–2711, 2012.

    Article  PubMed  Google Scholar 

  7. Oztekin, A., Delen, D., and Kong, Z. Y., Predicting the graft survival for heart-lung transplantation patients: an integrated data mining methodology. Int. J. Med. Inform. 78(12):E84–E96, 2009.

    Article  PubMed  Google Scholar 

  8. Wu, X. D., Kumar, V., Quinlan, J. R., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G. J., Ng, A., Liu, B., Yu, P. S., Zhou, Z. H., Steinbach, M., Hand, D. J., and Steinberg, D., Top 10 algorithms in data mining. Knowl. Inf. Syst. 14(1):1–37, 2008.

    Article  Google Scholar 

  9. Quinlan, J. R., C4.5: programs for machine learning. Morgan Kaufmann, Los Altos, 1993.

    Google Scholar 

  10. De, S. K., Biswas, R., and Roy, A. R., An application of intuitionistic fuzzy sets in medical diagnosis. Fuzzy Sets Syst. 117(2):209–213, 2001.

    Article  Google Scholar 

  11. Naghibi, S., Teshnehlab, M., and Shoorehdeli, M. A., Breast cancer classification based on advanced multi-dimensional fuzzy neural network. J. Med. Syst. 36(5):2713–2720, 2012.

    Article  PubMed  Google Scholar 

  12. Zadeh, L., A fuzzy sets. Inform. Comput. 8:338–353, 1965.

    Google Scholar 

  13. Desai, A. V., Ellis, E., Wheatley, J. R., and Grunstein, R. R., Fatal distraction: a case of fatal fall-asleep road accidents and their medicolegal outcomes. Med. J. Austral. 178:396–399, 2003.

    PubMed  Google Scholar 

  14. Barthel, S. W., and Strome, M. S., Obstructive sleep apnea and surgery. Med. Clin. N. Am. 83(1):85–96, 1999.

    Article  CAS  PubMed  Google Scholar 

  15. Hillman, D., Murphy, A., and Pezzullo, L., The economic cost of sleep disorders. Sleep 29(3):299–305, 2006.

    PubMed  Google Scholar 

  16. Rowley, J. A., Aboussouan, L. S., and Badr, M. S., The use of clinical prediction formulas in the evaluation of obstructive sleep apnea. NCBI 23:929–938, 2000.

    CAS  Google Scholar 

  17. Ryan, P. J., Hilton, M. F., Boldy, D. A., Evans, A., Bradbury, S., Sapiano, S., Prowse, K., and Cayton, R. M., Validation of british thoracic society guidelines for the diagnosis of the sleep apnoea/hypopnoea syndrome: Can polysomnography be avoided. Chest 50:972–975, 1995.

    CAS  Google Scholar 

  18. Yamashiro, Y., and Kryger, M. H., Nocturnal oximetry: is it a screening tool for sleep disorders. Sleep 18:167–171, 1999.

    Google Scholar 

  19. Rosenthal, L. D., and Diana, D. C., The epworth sleepiness scale in the identification of obstructive sleep apnea. J. Nerv. Ment. Dis. 196:429–431, 2008.

    Article  PubMed  Google Scholar 

  20. Jacob, S. V., Morielli, A., Mograss, M. A., Ducharme, F. M., Schloss, M. D., and Brouillette, R. T., Home testing for pediatric obstructive sleep apnea syndrome secondary to adenotonsillar hypertrophy. Pediatr. Pulmonol. 20:241–252, 1995.

    Article  CAS  PubMed  Google Scholar 

  21. Gurubhagavatula, I., and Maislin, G., An algorithm to stratify sleep apnea risk in a sleep sisorders clinic population. Am. J. Respir. Crit. Care Med. 170:371–376, 2001.

    Article  Google Scholar 

  22. Huang, S. H., Teng, N. C., Wang, K. J., Chen. K. H., Lee, H. C., and Wang, P. C., Use of oximetry as a screening tool for obstructive sleep apnea: a ca,e study in Taiwan. J. Med. Syst. In press, 2015.

  23. Chawla, N. V., Bowyer, K. W., Hall, L. O., and Kegelmeyer, W. P., SMOTE: Synthetic minor-ity over-sampling technique. J. Artif. Intell. Res. 16:321–357, 2002.

    Google Scholar 

  24. Chawla, N.V., Lazarevic, A., Hall, L.O., and Bowyer, K.W., SMOTE Boost: improving pre-diction of the minority class in boosting. Proc. 7th Europ.Conf. Principles Pract. Knowledge Discov.Database. 107–119, 2003.

  25. Lazarevic, A., Srivastava, J., and Kumar, V., Tutorial: data mining for analysis ofrare events: a case study in security, financial and medical applications. Proc. Pacific-Asia Conf. Knowledge Discov. Data Mining, 2004.

  26. Kubat, M., and Matwin, S., Addressing the curse of imbalanced training set: one-sided selection. Proc. 14th Int Conf. Mach. Learn, (ICML’97), 1997.

  27. Anonymous, Sleep-related breathing disorders in adults: recommendations for syndrome definition and measurement techniques in clinical research. Rep. Am. Acad. Sleep Med. Task Force. Sleep 22:667–689, 1999.

    Google Scholar 

  28. Stone, M., Cross-validatory choice and assessment of statistica predictions. J. R. Stat. Soc. 36:111–147, 1974.

    Google Scholar 

  29. Geisser, S., The predictive sample reuse method with applications. J. Am. Stat. Assoc. 70:320–328, 1975.

    Article  Google Scholar 

  30. Larson, S., The shrinkage of the coefficient of multiple correlation. J. Educ. Psychol. 22:45–55, 1931.

    Article  Google Scholar 

  31. Mosteller, F., and Turkey, J. W., Data analysis, including statistics. handbook of social psychology. Addison-Wesley, Reading, 1968.

    Google Scholar 

  32. Mosteller, F., and Wallace, D. L., Inference in an authorship problem. J. Am. Stat. Assoc. 58:275–309, 1963.

    Google Scholar 

  33. Zimmermann, H. J., Fuzzy sets, decision making, and expert systems (Vol. 10). Springer Science & Business Media, 2012.

  34. Corinna, C., and Vapnik, V. N., Support vector networks. Mach. Learn. 20:1–25, 1995.

    Google Scholar 

  35. LIBSVM., http://www.csie.ntu.edu.tw/~cjlin/libsvm/, 2015.

  36. See5., https://www.rulequest.com/see5-info.html, 2015.

  37. Pattaraintakorna, P., and Cerconeb, N., Integrating rough set theory and medical applications. Appl. Math. Lett. 21:400–403, 2008.

    Article  Google Scholar 

  38. Vogt, A., and Bared, J. G., Accident models for two-lane rural roads: segments and intersections. Transport. Res. Board Nationl. Acad. 1635:18–29, 1998.

    Article  Google Scholar 

  39. Huang, S. J., and Chiu, N. H., Optimization of analogy weights by genetic algorithm for software effort estimation. Inf. Softw. Technol. 48:1034–1045, 2006.

    Article  Google Scholar 

  40. John, B. D., Linda, M. S., and Brien, P. E., Predicting sleep apnea and excessive day sleepiness in the severely obese. Chest 123:1134–1141, 2003.

    Article  Google Scholar 

  41. Sharma, S. K., Malik, V., Vasudev, C., Banga, A., Mohan, A., Handa, K. K., et al., Prediction of obstructive sleep apnea in patients presenting to a tertiary care center. Sleep Breath 10:147–154, 2006.

    Article  CAS  PubMed  Google Scholar 

  42. The Huffington Post., 8 things that increase sleep apnea risk. Available from Internet:http://www.huffingtonpost.com/2013/10/06/sleep-apnea-risk_n_4018460.html, 2013.

  43. Bixler, E. O., Vgontzas, A. N., Kathy Tyson, T. T. H., and Kales, A., Effects of age on sleep apnea in men. Am. J. Respir. Crit. Care Med. 157(1):144–148, 1998.

    Article  CAS  PubMed  Google Scholar 

  44. Hla, K. M., Young, T. B., Bidwell, T., Palta, M., Skatrud, J. B., and Dempsey, J., Sleep apnea and hypertension-A population-based study. Ann. Intern. Med. 120:382–388, 1994.

    Article  CAS  PubMed  Google Scholar 

  45. Hader, C., Schroeder, A., Hinz, M., Micklefield, G. H., and Rasche, K., Sleep disordered breathing in the elderly: comparison of women and men. J. Physiol. Pharmacol. 56(4):85–91, 2005.

    PubMed  Google Scholar 

Download references

Acknowledgments

This work is partially supported by the National Science Council, R.O.C. (Taiwan), and National Taiwan University of Science and Technology - Taipei Medical University Joint Research Program (TMU-NTUST-102-06 & TMU-NTUST-101-07). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Authors and Affiliations

  1. Department of Industrial Management, National Taiwan University of Science and Technology, No.43, Sec. 4, Keelung Rd., Da’an Dist., Taipei, 106, Taiwan, Republic of China

    Kung-Jeng Wang & Kun-Huang Chen

  2. Department of Sleep Center, Taipei Medical University, Taipei, Taiwan, 110, Republic of China

    Shou-Hung Huang

  3. Department of Psychiatry & Psychiatric Research Center, Taipei Medical University, 110, Taipei, Taiwan, Republic of China

    Shou-Hung Huang

  4. School of Dentistry, College of Oral Medicine, Taipei Medical University, Taipei, 110, Taiwan, Republic of China

    Nai-Chia Teng

  5. Division of Oral Rehabilitation and Center of Pediatric Dentistry, Department of Dentistry, Taipei Medical University Hospital, Taipei, Taiwan, 110, Republic of China

    Nai-Chia Teng

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  1. Kung-Jeng Wang
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Correspondence to Kung-Jeng Wang.

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Wang, KJ., Chen, KH., Huang, SH. et al. A Prognosis Tool Based on Fuzzy Anthropometric and Questionnaire Data for Obstructive Sleep Apnea Severity. J Med Syst 40, 110 (2016). https://doi.org/10.1007/s10916-016-0464-y

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  • DOI: https://doi.org/10.1007/s10916-016-0464-y

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