Volume 18, Issue 12 (3-2020)
JRUMS 2020, 18(12): 1270-1286 |
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Faraji Gavgani L, Sarbakhsh P, AAsghari Jafarabadisghari Jafarabadi M, Shamshirgaran M. Application of Support Vector Machine for Detection of Functional Limitations in the Diabetic Patients of the Northwest of IRAN in 2017: A Descriptive Study. JRUMS 2020; 18 (12) :1270-1286
URL: http://journal.rums.ac.ir/article-1-4563-en.html
URL: http://journal.rums.ac.ir/article-1-4563-en.html
Abstract: (1923 Views)
Background and Objectives: Support vector machine (SVM) is a robust and effective statistical method for the diagnosis and prediction of clinical outcomes based on combinations of predictor variables. The aim of this study was to use SVM to detect the functional limitations in the diabetic patients and evaluate the accuracy of this diagnosis.
Materials and Methods: This descriptive study was conducted on 378 diabetic patients referred to the diabetic centers of Ardabil and Tabriz in 2014-2015. To classify the diabetic patients in terms of functional limitation, based on the demographic and clinical variables, SVM was used with RBF (radial basis function) kernel and the training and test validation method. Evaluation was performed based on diagnostic indices including sensitivity, specificity, accuracy and area under the ROC (receiver operating characteristic) curve.
Results: The results of SVM method showed that the classification accuracy, sensitivity, specificity of the SVM method in differentiating and correct diagnosis of functional limitations in the diabetic patients were 99%, 100% and 97%, respectively. The area under the ROC curve as the detection performance analysis of this model was 0.98.
Conclusion: In this study, SVM was used to classify the functional limitation status of the diabetic patients, and the results showed that the model had an acceptable performance. Considering the importance of classifying the medical outcomes correctly based on the combinations of predictor variables, the use of the methods such as SVM that are able to find optimal combinations could be helpful.
Key words: Data mining, SVM, Functional limitation, Classification, Kernel function.
Funding: This research was funded by Evidence-Based Medicine Center, Tabriz University of Medical Sciences.
Conflict of interest: None declared.
Ethical approval: The Ethics Committee of Tabriz University of Medical Sciences approved the study. (TBZMED.REC.1395.794).
How to cite this article: Faraji Gavgani L, Sarbakhsh P, Asghari Jafarabadi M, Shamshirgaran M. Application of Support Vector Machine for Detection of Functional Limitations in the Diabetic Patients of the Northwest of IRAN in 2015: A Descriptive Study. J Rafsanjan Univ Med Sci 2020; 18 (12): 1270-1286. [Farsi]
Materials and Methods: This descriptive study was conducted on 378 diabetic patients referred to the diabetic centers of Ardabil and Tabriz in 2014-2015. To classify the diabetic patients in terms of functional limitation, based on the demographic and clinical variables, SVM was used with RBF (radial basis function) kernel and the training and test validation method. Evaluation was performed based on diagnostic indices including sensitivity, specificity, accuracy and area under the ROC (receiver operating characteristic) curve.
Results: The results of SVM method showed that the classification accuracy, sensitivity, specificity of the SVM method in differentiating and correct diagnosis of functional limitations in the diabetic patients were 99%, 100% and 97%, respectively. The area under the ROC curve as the detection performance analysis of this model was 0.98.
Conclusion: In this study, SVM was used to classify the functional limitation status of the diabetic patients, and the results showed that the model had an acceptable performance. Considering the importance of classifying the medical outcomes correctly based on the combinations of predictor variables, the use of the methods such as SVM that are able to find optimal combinations could be helpful.
Key words: Data mining, SVM, Functional limitation, Classification, Kernel function.
Funding: This research was funded by Evidence-Based Medicine Center, Tabriz University of Medical Sciences.
Conflict of interest: None declared.
Ethical approval: The Ethics Committee of Tabriz University of Medical Sciences approved the study. (TBZMED.REC.1395.794).
How to cite this article: Faraji Gavgani L, Sarbakhsh P, Asghari Jafarabadi M, Shamshirgaran M. Application of Support Vector Machine for Detection of Functional Limitations in the Diabetic Patients of the Northwest of IRAN in 2015: A Descriptive Study. J Rafsanjan Univ Med Sci 2020; 18 (12): 1270-1286. [Farsi]
Type of Study: Applicable |
Subject:
Statistics& Epidemiology
Received: 2018/12/26 | Accepted: 2019/12/18 | Published: 2020/03/20
Received: 2018/12/26 | Accepted: 2019/12/18 | Published: 2020/03/20
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