Particle swarm optimization for feature selection with application in obstructive sleep apnea diagnosis (original) (raw)

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.

Access this article

Log in via an institution

Subscribe and save

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

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
  2. Chao-Ton Su
  3. Kun-Huang Chen
  4. Pa-Chun Wang

Corresponding author

Correspondence toLi-Fei Chen.

Rights 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

Keywords