Skip to main content

Advertisement

SpringerLink
Log in

Particle swarm optimization for feature selection with application in obstructive sleep apnea diagnosis

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Feature selection is a preprocessing step of data mining, in which a subset of relevant features is selected for building models. Searching for an optimal feature subset from a high-dimensional feature space is an NP-complete problem; hence, traditional optimization algorithms are inefficient in solving large-scale feature selection problems. Therefore, meta-heuristic algorithms are extensively adopted to effectively address feature selection problems. In this paper, we propose an analytical approach by integrating particle swarm optimization (PSO) and the 1-NN method. The data sets collected from UCI machine learning databases were used to evaluate the effectiveness of the proposed approach. Implementation results show that the classification accuracy of the proposed approach is significantly better than those of BPNN, LR, SVM, and C4.5. Furthermore, the proposed approach was applied to an actual case on the diagnosis of obstructive sleep apnea (OSA). After implementation, we conclude that our proposed method can help identify important factors and provide a feasible model for diagnosing medical disease.

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

Similar content being viewed by others

References

  1. Ai J, Kachitvichyanukul V (2009) A particle swarm optimization for the vehicle routing problem with simultaneous pickup and delivery. Comput Oper Res 36(5):1693–1702

    Article  MATH  Google Scholar 

  2. Anonymous (1999) Sleep-related breathing disorders in adults: recommendations for syndrome definition and measurement techniques in clinical research. Sleep 22(5):667–689

    Google Scholar 

  3. Chuang LY, Chang HW, Yang CH (2008) Improved binary PSO for feature selection using gene expression data. Comput Biol Chem 32(1):29–38

    Article  MATH  Google Scholar 

  4. Corinna C, Vapnik VN (1995) Support vector networks. Mach Learn 20(3):1–25

    Google Scholar 

  5. Duda RO, Hart PE, Stork DG (2000) Pattern classification. Wiley, NJ

    Google Scholar 

  6. Engelbrecht AP (2006) Fundamentals of computational swarm intelligence. Wiley, NJ

    Google Scholar 

  7. Fix FE, Hodges JL (1951) Discriminatory analysis-nonparametric discrimination: consistency properties. In: School of Mathematical Sciences, vol 4, pp 261–279

  8. Gertheiss J, Titz G (2009) Feature selection and weighting by nearest neighbor ensembles. Chemometr Intell Lab 99(1):30–38

    Article  Google Scholar 

  9. Gheyas IA, Smith LS (2010) Feature subset selection in large dimensionality domains. Pattern Recogn 43(1):5–13

    Article  MATH  Google Scholar 

  10. Gliklich RE, Wang PC (2002) Validation of the snore outcomes survey for patients with sleep-disordered breathing. Arch Otolaryngol 128(7):819–824

    Google Scholar 

  11. Gurubhagavatula I, Maislin G, Pack AL (2001) An algorithm to stratify sleep apnea risk in a sleep disorders clinic population. Am J Resp Care 164(10):371–376

    Google Scholar 

  12. Ho TK, Basu M (2002) Complexity measures of supervised classification problems. IEEE T Pattern Anal 24(3):289–300

    Article  Google Scholar 

  13. Hu X, Eberhart R (2002) Multiobjective optimization using dynamic neighborhood particle swarm optimization. IEEE T Evolut Comput 10:1677–1681

    Google Scholar 

  14. Jacob SV, Morielli A, Mograss MA, Ducharme FM, Schloss MD, Brouillette RT (1995) Home testing for pediatric obstructive sleep apnea syndrome secondary to adenotonsillar hypertrophy. Pediatr Pulm 20(4):241–252

    Article  Google Scholar 

  15. Jiang Z, Yamauchi K, Yoshioka K, Aoki K, Kuroyanagi S, Iwata A, Yang J, Wang K (2006) Support vector machine-based feature selection for classification of liver fibrosis grade in chronic hepatitis C. J Med Syst 20:389–394

    Article  Google Scholar 

  16. Kapsimalis F, Kryger MH (2002) Gnder and ostructive sleep apnea syndrome. Sleep 25(4):497–504

    Google Scholar 

  17. Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural network, vol 4, pp 1942–1948

  18. Kennedy J, Eberhart RC (1997) A discrete binary version of the particle swarm algorithm. In: International conference systems man cybernet, vol 5, pp 4104–4108

  19. Kennedy J, Eberhart RC, Shi Y (2001) Swarm intelligence. Morgan Kaufman, Los Altos

    Google Scholar 

  20. Kubat M, Matwin S (1997) Addressing the curse of imbalanced training set: One-sided selection. In: Proceedings 14th international conference on machine learning (ICML ‘97)

  21. Lee JH, Cha GH (1999) A model for K-nearest neighbor query processing cost in multidimensional data space. Inform Process Lett 69(2):69–76

    Article  MathSciNet  Google Scholar 

  22. Lewis CA, Eaton TE, Fergusson W, Whyte KF, Garrett JE, Kolbe J (2003) Home overnight pulse oximetry in patients with COPD: more than one recording may be needed. Chest 123(4):1127–1133

    Article  Google Scholar 

  23. Maximiliano S, Yuji T (2003) Conformational analyses and SAR Studies of antispermatogenic hexahydroindenopyridines. J Mol Struct (Theochem) 633(2–3):93–104

    Google Scholar 

  24. Netzer N, Eliasson AH, Netzer C, Krisco DA (2001) Overnight pulse oximetry for sleep-disordered breathing in adults-a review. Chest 120(2):625–633

    Article  Google Scholar 

  25. Pang KP, Terris DJ, Podolsky R (2006) Screening for obstructive sleep apnea: an evidence-based analysis. Am J Otolaryng 27(2):112–118

    Article  Google Scholar 

  26. Qureshi A, Ballard RD (2003) Obstructive sleep apnea. J Allergy Clin Immun 112(4):643–651

    Article  Google Scholar 

  27. Rangayyan RM, Banik S, Desautels JEL (2010) Computer-aided detection of architectural distortion in prior mammograms of interval cancer. J Digit Imaging 23(5):611–631

    Article  Google Scholar 

  28. Rosenthal LD, Diana DC (2008) The Epworth sleepiness scale in the identification of obstructive sleep apnea. J Nerv Ment Dis 196(5):429–431

    Article  Google Scholar 

  29. Schäfer H, Ewig S, Hasper E, Lüderitz B (1997) Predictive diagnostic value of clinical assessment and nonlaboratory monitoring system recordings in patients with symptoms suggestive of obstructive sleep apnea syndrome. NCBI 64(3):194–199

    Google Scholar 

  30. Shepertycky MR, Banno K, Kryger MH (2005) Differences between men and women in clinical presentation of patients diagnosed with obstructive sleep apnea syndrome. Sleep 28(3):309–314

    Google Scholar 

  31. Shi Y, Eberhart RC (1998) A modified particle swarm optimizer. In: IEEE International conference on evolutionary computation anchorage Alaska, pp 69–73

  32. Tan S (2005) Neighbor-weighted k-nearest neighbor for unbalanced text corpus. Expert Syst Appl 28(3):667–671

    Article  Google Scholar 

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

    Article  Google Scholar 

  34. Yeh W, Huang SW, Li PC (2003) Liver fibrosis grade classification with B-mode ultrasound. Ultrasound Med Biol 29(9):1229–1235

    Article  Google Scholar 

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

    Article  Google Scholar 

  36. Young T, Peppard PE, Gottlieb DJ (2002) Epidemiology of obstructive sleep apnea: a population health perspective. Am J Resp Care 165:1217–1239

    Article  Google Scholar 

  37. Ziari I, Jalilian A (2010) New approach for allocation and sizing of multiple active power-line conditioners. IEEE T Power Deliver 25(2):1026–1035

    Article  Google Scholar 

Download references

Acknowledgments

This work is partially supported by grants from National Science Council, Taiwan, R.O.C.

Author information

Authors and Affiliations

  1. Graduate Program of Business Management, Fu-Jen Catholic University, No. 510, Zhongzheng Rd., Xinzhung Dist., New Taipei City, 24205, Taiwan, R.O.C

    Li-Fei Chen

  2. Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu, Taiwan, R.O.C

    Chao-Ton Su & Kun-Huang Chen

  3. Department of Otolaryngology, Cathay General Hospital, Taipei, Taiwan, R.O.C

    Pa-Chun Wang

  4. School of Medicine, Fu Jen Catholic University, Taipei, Taiwan, R.O.C

    Pa-Chun Wang

  5. Department of Public Health, China Medical University, Taichung, Taiwan, R.O.C

    Pa-Chun Wang

Authors
  1. Li-Fei Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chao-Ton Su
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kun-Huang Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Pa-Chun Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Li-Fei Chen.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chen, LF., Su, CT., Chen, KH. et al. Particle swarm optimization for feature selection with application in obstructive sleep apnea diagnosis. Neural Comput & Applic 21, 2087–2096 (2012). https://doi.org/10.1007/s00521-011-0632-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-011-0632-4

Keywords

Search

Navigation