İMRAN KURT ÖMÜRLÜ, MEVLÜT TÜRE

4434

Tıp öğrencilerinde alkol kullanımını etkileyen faktörlerin belirlenmesinde yapay sinir ağları ile lojistik regrasyon analizi'nin karşılaştırılması

Amaç: Bu çalışmada, öğrencilerin alkol kullanımını etkileyen faktörler lojistik regresyon analizi (LR) ve yapay sinir ağları (YSA) ile incelendi ve bu yöntemlerin alkol kullanan ve kullanmayan öğrencileri ayırmadaki etkinlikleri ROC (Receiver Operating Characteristic) eğrisi yöntemiyle karşılaştırıldı. Çalışma Planı: Çalışmada, 2003-2004 eğitim-öğretim yılında Trakya Üniversitesi Tıp Fakültesi’nin 1-4 sınıflarında okuyan öğrencilere Frontal Lob Kişilik Ölçeği ve alkol kullanma alışkanlıklarını belirlemeye yönelik bir anket uygulandı. Bulgular: Lojistik regresyon analizinde, ders dı.ındaki zamanlarda bar, disko, kafe ya da kahvehaneye gitme (OR=1.920; p
Anahtar Kelimeler:

Karşılaştırmalı çalışma, Regresyon analizi, ROC eğrisi, Sigara içme, Anketler, Öğrenciler, Sinir ağı, Lojistik modeller

Comparison of artificial neural networks and logistic regression analysis in determining factors affecting alcohol consumption among medicine students

Objectives: The factors that affect students&#8217; alcohol use behaviors were examined by logistic regression analysis and artificial neural networks and the efficiency of these methods in identifying alcohol users and non-users was compared using the receiver operating characteristics (ROC) curve method. Study Design: Graduate students of 1-4 years in Trakya University Medical Faculty (2003-2004) were administered a questionnaire to predict their alcohol use behaviors and were assessed with the Frontal Lobe Personality Scale. Results: Logistic regression analysis showed that the following variables highly affected alcohol use behaviors of the students: visiting bars, discos or cafes in their spare time (OR=1.920; p<0.05), the importance of religion (OR=0.454; p<0.001), the number of alcohol-user friends (OR=2.441; p<0.001), insistence of friends on taking alcohol (OR=1.557;p<0.01), and impulsiveness (OR=1.826; p<0.001). Comparison between logistic regression analysis and artificial neural Networks showed no differences in terms of the areas under the ROC curves of hyperbolic tangent-hyperbolic tangent function and hyperbolic tangent-logistic function artificial neural networks, but these models showed statistically larger areas than the other models. Conclusion: It may be necessary to take into account the advantages and disadvantages of artificial neural networks and logistic regression in classification and modelling, and to use artificial neural networks to eliminate insignificant variables of logistic regression analysis.
Keywords:

Comparative Study, Regression Analysis, ROC Curve, Smoking, Questionnaires, Students, Nerve Net, Logistic Models,

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  • 1. Haykin S. Neural network: a comprehensive foundation. Upper Saddle River NJ. 2nd ed. New York: Prentice-Hall; 1999.
  • 2. Efe Ö, Kaynak O. Yapay sinir ağları ve uygulamaları. 1. Baskı. İstanbul: Bo¤aziçi Üniversitesi; 2000. s. 148.
  • 3. Tu JV. Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. J Clin Epidemiol 1996;49:1225-31.
  • 4. Dinn WM, Aycicegi A, Harris CL. Cigarette smoking in a student sample: neurocognitive and clinical correlates. Addict Behav 2004;29:107-26.
  • 5. Ayçiçeği A, Dinn WM, Harris CL. Prefrontal lob nöropsikolojik test bataryası: sağlıklı yetişkinlerden elde edilen test sonuçları. Psikoloji Çalışmaları 2003;23:1-26.
  • 6. Özdamar K. Paket programlarla istatistiksel veri analizi-I. Eskiflehir: Kaan Kitabevi; 1999.
  • 7. Tatlıdil H. Uygulamalı çok değişkenli istatistiksel analiz. Ankara: Akademi Matbaası; 1996.
  • 8. Armitage P, Berry G. Statistical methods in medical research. 3rd ed. New York: Blackwell Science Ltd; 1994.
  • 9. Hosmer, DW, Lemeshow S. Applied logistic regression. 2nd ed. New York: John Wiley & Sons; 2000.
  • 10. Kleinbaum DG. Logistic regression: a self-learning text. 2nd ed. New York: Springer-Verlag. 1994.
  • 11. Sharma S. Applied multivariate techniques. 5th ed. New York: John Wiley & Sons; 1996.
  • 12. Gaudart J, Giusiano B, Huiart L. Comparison of the performance of multi-layer perceptron and linear regression for epidemiological data. Computational Statistics and Data Analysis 2004;44:547-70.
  • 13. Badr EA, Nasr GE, Joun C. Backpropagation neural networks for modeling gasoline consumption. Energy Conversion and Management 2003;44:893-905.
  • 14. Francis L. The basics of neural networks demystified. Contingencies November/December; 2001. p. 56-61. Available from: http://www.contingencies. org/novdec01/workshop.pdf.
  • 15. Hassoun MH. Fundamentals of artificial neural networks. Cambridge, Mass: MIT Press; 1995.
  • 16. Kröse B, Smagt P. An introduction to neural networks. Amsterdam: The University of Amsterdam;1996.
  • 17. Rojas R. Neural networks: a systematic introduction. Berlin: Springer; 1991.
  • 18. Sundararajan N, Saratchandran P. Parallel architectures for artificial neural networks: paradigms and implementations. California: IEEE Computer Society Press; 1998.
  • 19. Principe J, Euliano NR, Lefebvre WC. Neural and adaptive systems: fundamentals through simulations. New York: John Wiley & Sons Inc; 1999.
  • 20. Özdamar K. SPSS ile biyoistatistik. Eskiflehir: Kaan Kitabevi; 2003.
  • 21. Dirican A. Evaluation of the diagnostic test’s performance and their comparisons. Cerrahpafla J Med2001;32:25-30.
  • 22. Swaving M, van Houwelingen H, Ottes FP, Steerneman T. Statistical comparison of ROC curves from multiple readers. Med Decis Making 1996;16:143-52.
  • 23. Hanley JA, McNeil BJ. A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology 1983;148:839-43.
  • 24. Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 1982;143:29-36.
  • 25. McClish DK. Comparing the areas under more than two independent ROC curves. Med Decis Making 1987;7:149-55.
  • 26. Köknel Ö. Alkol ve madde bağımlılığı altkültürü. Bağımlılık Dergisi 2001;2. Available from: http://www.bagimlilik.net/sayi4/alkol_altkulturu. pdf.
  • 27. Drobes J. Concurrent alcohol and tobacco dependence mechanisms and treatment. Alcohol Research & Health 2002;26:136-42.
  • 28. Öncü F, Ögel K, Çakmak D. Alkol kültürü-1: tarihsel süreç ve meyhane kültürü. Bağımlılık Dergisi 2001;2. Available from: http://www.bagimlilik.net/sayi5/ alkol_kulturu1.pdf.
  • 29. Hefleminia T, Çalışkan D, Işık A. Ankara’da yüksek öğretim öğrenci yurtlarında kalan öğrencilerin beslenme sorunları. İbni Sina Tıp Dergisi 2002;7:155-66.
  • 30. Kirkcaldy BD, Siefen G, Surall D, Bischoff RJ. Predictors of drug and alcohol abuse among children and adolescents. Personality and Individual Differences 2004;36:247-65.
  • 31. Türkcan A. Alkol kullanma iste¤inin (craving)mekanizması. Bağımlılık Dergisi 2002;3:37-42.
  • 32. Akvardar Y. Alkol bağımlılığında kişilik özellikleri. Bağımlılık Dergisi 2003;4:26-30.
  • 33. Rowland T, Ohno-Machado L, Ohrn A. Comparison of multiple prediction models for ambulation following spinal cord injury. Proc AMIA Symp 1998:528-32.
  • 34. Ottenbacher KJ, Smith PM, Illig SB, Linn RT, Fiedler RC, Granger CV. Comparison of logistic regression and neural networks to predict rehospitalization in patients with stroke. J Clin Epidemiol 2001;54:1159-65.
  • 35. Ottenbacher KJ, Linn RT, Smith PM, Illig SB, Mancuso M, Granger CV. Comparison of logistic regression and neural network analysis applied to predicting living setting after hip fracture. Ann Epidemiol 2004;14:551-9.
  • 36. Hajmeer M, Basheer I. Comparison of logistic regression and neural network-based classifiers for bacterial growth. Food Microbiology 2003;20:43-55.
  • 37. Dreiseitl S, Ohno-Machado L. Logistic regression and artificial neural network classification models: a methodology review. J Biomed Inform 2002; 35:352-9.
  • 38. Nguyen T, Malley R, Inkelis S, Kuppermann N. Comparison of prediction models for adverse outcome in pediatric meningococcal disease using artificial neural network and logistic regression analyses. J Clin Epidemiol 2002;55:687-95.
  • 39. Remzi M, Anagnostou T, Ravery V, Zlotta A, Stephan C, Marberger M, et al. An artificial neural network to predict the outcome of repeat prostate biopsies. Urology 2003;62:456-60.
  • 40. Ergun UU, Serhatlioglu S, Hardalac F, Guler I. Classification of carotid artery stenosis of patients with diabetes by neural network and logistic regression. Comput Biol Med 2004;34:389-405.
  • 41. Yamamura S, Kawada K, Takehira R, Nishizawa K, Katayama S, Hirano M, et al. Artificial neural network modeling to predict the plasma concentration of aminoglycosides in burn patients. Biomed Pharmacother 2004;58:239-44.
  • 42. Manel S, Dias JM, Ormerod SJ. Comparing discriminant analysis, neural networks and logistic regression for predicting species distributions: a case study with a Himalayan river bird. Ecological Modelling 1999;120:337-47.
Trakya Üniversitesi Tıp Fakültesi Dergisi-Cover
  • ISSN: 1301-3149
  • Yayın Aralığı: Yılda 2 Sayı
  • Başlangıç: 2018
  • Yayıncı: -
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