A Prognosis Tool Based on Fuzzy Anthropometric and Questionnaire Data for Obstructive Sleep Apnea Severity
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.
KeywordsDiagnosis model Fuzzy decision tree Obstructive sleep apnea
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.
Compliance with Ethical Standards
Conflict of Interest
This study has no conflict of interest to any parties/agencies.
- 3.Tan, A. C., and Gilbert, D., Ensemble machine learning on gene expression data for cancer classification. Appl. Bioinform. 2:S75–83, 2003.Google Scholar
- 9.Quinlan, J. R., C4.5: programs for machine learning. Morgan Kaufmann, Los Altos, 1993.Google Scholar
- 12.Zadeh, L., A fuzzy sets. Inform. Comput. 8:338–353, 1965.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.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.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
- 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.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- 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
- 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.Google Scholar
- 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.
- 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.