Written by Nasrolah Nasr HeidarAbadi, Laleh Hakemi, Pirhossein Kolivand, Reza Safdari, Marjan Ghazi Saeidi
Parent Category: Year 2017, Volume 9
Category: Volume 9, Issue 7, July 2017
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.
Keywords: Bayesian Networks, Decision Tree, Support Vector Machines, Neural Networks, Spinal cord injury, pain; Accuracy
Volume 12, Issue 4, October-December 2020
The worldwide spread of COVID-19 as an emerging, rapidly evolving situation, and the dramatic need of urgent medicine or vaccine, has rapidly brought new hypotheses for pathophysiology and potential medicinal agents to the fore. It is crucial that the research community provide a way to publish this research in a timely manner.
To contribute to this important public health discussion, the Electronic Physician Journal is excited to announce a fast-track procedure to help researchers publish their articles on COVID-19 related subjects that fall under the broad definition of public health, internal medicine, and pharmacology. We are especially welcome to all hypotheses about the pathological basis of the COVID-19 infection and the possible characteristics of potential medicine and vaccine. Submit your manuscript here
The 6th World Conference on Research Integrity (WCRI) is to be held on June 2-5, 2019 in Hong Kong.
The WCRI is the largest and most significant international conference on research integrity. Since the first conference in Lisbon in 2007, it has given researchers, teachers, funding agencies, government officials, journal editors, senior administrators, and research students opportunities to share experiences and to discuss and promote integrity in research. Read more:
TDR Clinical Research and Development Fellowships
Call for applications
Deadline for submission: 7 March 2019, 16:00 (GMT)
TDR provides fellowships for early- to mid-career researchers and clinical trial staff (e.g. clinicians, pharmacists, medical statisticians, data managers, other health researchers) in low- and middle-income countries (LMICs) to learn how to conduct clinical trials. Read more:
Meta-Analysis Workshops in New York, USA, and London, UK, in April and May 2019
Don't miss this exceptional opportunity to learn how to perform and report a Meta-analysis correctly. Two Meta-analysis workshops are organized in April and May 2019 by Dr. Michael Borenstein in New York, USA (April 08-10, 2019) and London, UK (May 27-29).
About the Instructor
Dr. Michael Borenstein, one of the authors of Introduction to Meta-Analysis, is widely recognized for his ability to make statistical concepts accessible to researchers as well as to statisticians. He has lectured widely on meta-analysis, including at the NIH, CDC, and FDA. Read more: