Comparing performances of intelligent classifier algorithms for predicting type of pain in patients with spinal cord injury
Keywords:
Bayesian Networks, Decision Tree, Support Vector Machines, Neural Networks, Spinal cord injury, pain; AccuracyAbstract
Background and aim: In this study, performances of classification techniques were compared in order to predict type of pain in patients with spinal cord injury. Pain is one of the main problems in people with spinal cord injury. Identifying the optimal classification technique will help improve decision support systems in clinical settings.
Methods: A descriptive retrospective analysis was performed in 253 patients. We compared performances of "Bayesian Networks", "Decision Tree", neural networks: “Multi-Layer Perceptron” (MLP), and "Support Vector Machines” (SVM). Predictor variables were collected in data set in SCI patients referred to Shefa Neuroscience Research Center, Tehran, Iran from 2010 through 2016. Performances of classification techniques were compared using ”Accuracy”, ”Sensitivity or True Positive Rate” (TPR), ”Specificity or True Negative Rate” (SPC), ”Positive Predictive Value” (PPV), ”Negative Predictive Value” (NPV).
Results: MLP with Boosting technique was found to have the best accuracy (91%), best sensitivity (89%), best specificity (95%) best PPV (91%), and best NPV (96%) to predict spinal cord injury in this data set, given its good classificatory performance.
Conclusion: Computer-aided decision support systems (CAD) are dependent on a wide range of classification methods such as statistical methods, Bayesian methods, deductive classifiers based on the state or case, decision-making trees and neural networks: Multi-Layer Perceptron. Neural network classifiers especially, are very popular choices for medical decision-making, with proven effectiveness in the clinical field.
References
Bonica JJ, Fishman S, Ballantyne J, Rathmell JP. Bonica’s management of pain. Lippincott Williams &
Wilkins; 2010.
Turk DC, Dworkin RH. What should be the core outcomes in chronic pain clinical trials? Arthritis Res
Ther. 2004; 6(4): 151–4. doi: 10.1186/ar1196. PMID: 15225358, PMCID: PMC464897.
Mohammadzadeh F, Faghihzadeh S, Asadi Lari M, Vaez Mahdavi MR, Arab Kheradmand J, Noorbala AA,
et al. A Fairly Comprehensive Survey of Chronic Pain in Iranian Population: Prevalence, Risk Factors, and
Impact on Daily Life. Health Scope. 2015; 4(3); e25467. doi: 10.17795/jhealthscope-25467.
Breivik H, Borchgrevink P, Allen S, Rosseland L, Romundstad L, Hals EK, et al. Assessment of pain. Br J
Anaesth. 2008; 101(1): 17–24. doi: 10.1093/bja/aen103. PMID: 18487245.
Pain management. In: Wikipedia, the free encyclopedia. 2015. Available from:
https://en.wikipedia.org/w/index.php?title=Pain_management&oldid=673213088.
Bassols A, Bosch F, Campillo M, Cañellas M, Baños JE. An epidemiological comparison of pain
complaints in the general population of Catalonia (Spain). Pain. 1999; 83(1): 9–16. doi: 10.1016/S0304- 3959(99)00069-X. PMID: 10506667.
Sternbach RA. Survey of Pain in the United States: The Nuprin Pain Report. Clinical Journal of Pain. 2016.
Siddall P, Taylor D, Cousins M. Classification of pain following spinal cord injury. Spinal Cord. 1997;
(2): 69–75. doi: 10.1038/sj.sc.3100365. PMID: 9044512.
Westgren N, Levi R. Quality of life and traumatic spinal cord injury. Arch Phys Med Rehabil. 1998; 79:
–9. doi: 10.1016/S0003-9993(98)90240-4. PMID: 9821906.
Breivik H, Borchgrevink PC, Allen SM, Rosseland LA, Romundstad L, Hals EK, et al. Assessment of pain.
Br J Anaesth. 2008; 101(1): 17–24. doi: 10.1093/bja/aen103. PMID: 18487245.
Roshanov PS, Misra S, Gerstein HC, Garg AX, Sebaldt RJ, Mackay JA, et al. Computerized clinical
decision support systems for chronic disease management: a decision-maker-researcher partnership
systematic review. Implement Sci. 2011; 6: 92. doi: 10.1186/1748-5908-6-92. PMID: 21824386, PMCID:
PMC3170626.
Neill DB. Using Artificial Intelligence to Improve Hospital Inpatient Care. IEEE Intelligent Systems. 2013;
(2): 92–5. doi: 10.1109/MIS.2013.51.
Friedman N, Geiger D, Goldszmidt M. Bayesian network classifiers. Mach Learn. 1997; 29(2–3): 131–63.
doi: 10.1023/A:1007465528199.
Basheer IA, Hajmeer M. Artificial neural networks: fundamentals, computing, design, and application. J
Microbiol Methods. 2000; 43(1): 3–31. doi: 10.1016/S0167-7012(00)00201-3. PMID: 11084225.
Baxt WG. Use of an artificial neural network for the diagnosis of myocardial infarction. Ann Intern Med.
; 115(11): 843–8. doi: 10.7326/0003-4819-115-11-843. PMID: 1952470.
Forsström JJ, Dalton KJ. Artificial neural networks for decision support in clinical medicine. Ann Med.
; 27(5): 509-17. doi: 10.3109/07853899509002462. PMID: 8541025.
Kurt I, Ture M, Kurum AT. Comparing performances of logistic regression, classification and regression
tree, and neural networks for predicting coronary artery disease. Expert Syst Appl. 2008; 34(1): 366–74.
doi: 10.1016/j.eswa.2006.09.004.
Delen D, Walker G, Kadam A. Predicting breast cancer survivability: a comparison of three data mining
methods. Artif Intell Med. 2005; 34(2): 113–27. doi: 10.1016/j.artmed.2004.07.002. PMID: 15894176.
Khalilinezhad M, Minaei B, Vernazza G, Dellepiane S. Prediction of healthy blood with data mining
classification by using Decision Tree, Naive Baysian and SVM approaches. In: International Society for
Optics and Photonics. 2015: 94432. doi: 10.1117/12.2179871.
Stärk KD, Pfeiffer DU. The application of non-parametric techniques to solve classification problems in
complex data sets in veterinary epidemiology–an example. Intell Data Anal. 1999; 3(1): 23–35. doi:
1016/S1088-467X(99)00003-7.
Lee HK. Model selection for neural network classification. J Classif. 2001; 18(2): 227–43.
Amato F, López A, Peña-Méndez EM, Vaňhara P, Hampl A, Havel J. Artificial neural networks in medical
diagnosis. Journal of Applied Biomedicine. 2013; 11(2): 47–58. doi: 10.2478/v10136-012-0031-x.
Haykin S. A comprehensive foundation. Neural Networks. 2004.
Friedman N, Geiger D, Goldszmidt M. Bayesian network classifiers. Mach Learn. 1997; 29(2–3): 131–63.
doi: 10.1023/A:1007465528199.
Bayesian network - Wikipedia. 2017. Available from: https://en.wikipedia.org/wiki/Bayesian_network.
Kikuti D, Cozman FG, Shirota Filho R. Sequential decision making with partially ordered preferences.
Artif Intell. 2011; 175(7): 1346–65. doi: 10.1016/j.artint.2010.11.017.
Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995; 20(3): 273–97. doi:
1007/BF00994018.
Ben-Hur A, Horn D, Siegelmann HT, Vapnik V. Support vector clustering. J Mach Learn Res. 2001; 2:
–37.
Olson DL, Delen D. Advanced data mining techniques. Springer Science & Business Media. 2008.
Tashani OA, Alabas OA, Johnson MI. Development of a Short-Form McGill Pain Questionnaire for use in
Libyan Populations. Pain and Rehabilitation. 2016; 2016(41): 25–30.
Breiman L. Bias, variance, and arcing classifiers. 1996.
Zhaoqian C, Hong L, Rong Z, Shifu C. A hybrid algorithm for multi-concept acquisition and its
application. Chin J Comput. 1996; 10.
Breiman L. Bagging predictors. Mach Learn. 1996; 24(2): 123–40. doi: 10.1007/BF00058655.
Mazurowski MA, Habas PA, Zurada JM, Lo JY, Baker JA, Tourassi GD. Training Neural Network
Classifiers for Medical Decision Making: The Effects of Imbalanced Datasets on Classification
Performance. Neural Netw. 2008; 21(2–3): 427–36. doi: 10.1016/j.neunet.2007.12.031. PMID: 18272329,
PMCID: PMC2346433.
Published
Issue
Section
License
Copyright (c) 2020 KNOWLEDGE KINGDOM PUBLISHING
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
