Volume 77, Issue 1 (April 2019)                   Tehran Univ Med J 2019, 77(1): 33-40 | Back to browse issues page

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Hashemian A H, Manochehri S, Afshari D, Manochehri Z, Salari N, Shahsavari S. Prognosis of multiple sclerosis disease using data mining approaches random forest and support vector machine based on genetic algorithm. Tehran Univ Med J. 2019; 77 (1) :33-40
URL: http://tumj.tums.ac.ir/article-1-9561-en.html
1- Research Center for Environmental Determinants of Health (RCEDH), Kermanshah University of Medical Sciences, Kermanshah, Iran. Department of Biostatistics, School of Health, Kermanshah University of Medical Sciences, Kermanshah, Iran.
2- Department of Biostatistics, Students Research Committee, School of Health, Kermanshah University of Medical Sciences, Kermanshah, Iran. , saramanochehri0098@gmail.com
3- Department of Neurology, Faculty of Medicine, Kermanshah University of Medical Sciences, Kermanshah, Iran.
4- Department of Biostatistics, Student Research Committee, Hamadan University of Medical Sciences, Hamadan, Iran.
5- Department of Biostatistics, School of Health, Kermanshah University of Medical Sciences, Kermanshah, Iran.
6- Department of Health Information Management, School of Paramedical, Kermanshah University of Medical Sciences, Kermanshah, Iran.
Abstract:   (194 Views)
Background: Multiple sclerosis (MS) is a degenerative inflammatory disease which is most commonly diagnosed by magnetic resonance imaging (MRI). But, since the MRI device uses of a magnetic field, if there are metal objects in the patient's body, it can disrupt the health of the patient, the functioning of the MRI, and distortion in the images. Due to limitations of using MRI device, screening seems necessary for those patients who have metal objects in their bodies. Therefore, this study is carried out to compare two models: support vector machine and random forest.
Methods: This analytical-modelling research was implemented on MS data collection, the specifications of which are recorded in health registry system in School of Public Health, Kermanshah University of Medical Sciences, Iran, from May 2017 to August 2018. For the purpose of this study, a total of 317 subjects were selected as a sample; 188 subjects were diagnosed with MS and 128 subjects showed no symptoms of MS. In order to fit the support vector machine (SVM) model, radial basis kernel function was used. The parameters of this machine were optimized with genetic algorithm. After this step, the support vector machine and random forest (RF) were compared with respect to three factors: accuracy, sensitivity, and specificity.
Results: Based upon the obtained results of study, accuracy, sensitivity, and specificity of SVM were 0.79, 0.80, and 0.78, respectively. In comparison, accuracy, sensitivity, and specificity of RF were found to be 0.76, 0.81, and 0.70, respectively.
Conclusion: In general, both models which were compared in current study showed desirable performance; however, in term of accuracy, as an important criteria for performance comparison in this area of research, it can be argued that support vector machine can do better than random forest in diagnosing multiple sclerosis.
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Type of Study: Original Article |

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