Comparing performances of intelligent classifier algorithms for predicting type of pain in patients with spinal cord injury
| Autor: | Marjan Ghazi Saeidi, Reza Safdari, Nasrolah Nasr HeidarAbadi, Pirhossein Kolivand, Laleh Hakemi |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Decision support system medicine.medical_specialty Neural Networks Computer science Bayesian probability Decision Tree Decision tree Spinal cord injury Machine learning computer.software_genre 03 medical and health sciences 0302 clinical medicine Physical medicine and rehabilitation Support Vector Machines medicine pain Accuracy lcsh:R5-920 Artificial neural network business.industry Bayesian network Perceptron medicine.disease Support vector machine 030104 developmental biology Bayesian Networks Original Article Artificial intelligence lcsh:Medicine (General) business computer 030217 neurology & neurosurgery |
| Zdroj: | Electronic Physician Electronic Physician, Vol 9, Iss 7, Pp 4847-4852 (2017) |
| ISSN: | 2008-5842 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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