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Original Articles
Diabetic peripheral neuropathy class prediction by multicategory support vector machine model: a cross-sectional study
Maryam Kazemi, Abbas Moghimbeigi, Javad Kiani, Hossein Mahjub, Javad Faradmal
Epidemiol Health. 2016;38:e2016011.   Published online March 24, 2016
DOI: https://doi.org/10.4178/epih.e2016011
  • 16,193 View
  • 205 Download
  • 19 Web of Science
  • 18 Crossref
AbstractAbstract PDF
Abstract
OBJECTIVES
Diabetes is increasing in worldwide prevalence, toward epidemic levels. Diabetic neuropathy, one of the most common complications of diabetes mellitus, is a serious condition that can lead to amputation. This study used a multicategory support vector machine (MSVM) to predict diabetic peripheral neuropathy severity classified into four categories using patients’ demographic characteristics and clinical features.
METHODS
In this study, the data were collected at the Diabetes Center of Hamadan in Iran. Patients were enrolled by the convenience sampling method. Six hundred patients were recruited. After obtaining informed consent, a questionnaire collecting general information and a neuropathy disability score (NDS) questionnaire were administered. The NDS was used to classify the severity of the disease. We used MSVM with both one-against-all and one-against-one methods and three kernel functions, radial basis function (RBF), linear, and polynomial, to predict the class of disease with an unbalanced dataset. The synthetic minority class oversampling technique algorithm was used to improve model performance. To compare the performance of the models, the mean of accuracy was used.
RESULTS
For predicting diabetic neuropathy, a classifier built from a balanced dataset and the RBF kernel function with a one-against-one strategy predicted the class to which a patient belonged with about 76% accuracy.
CONCLUSIONS
The results of this study indicate that, in terms of overall classification accuracy, the MSVM model based on a balanced dataset can be useful for predicting the severity of diabetic neuropathy, and it should be further investigated for the prediction of other diseases.
Summary

Citations

Citations to this article as recorded by  
  • The mediating effect of self-efficacy on the relationship between social support and medication adherence in adults with type 2 diabetes
    Khadijeh Khalili Azar, Amirreza Mirzaei, Ali-Reza Babapour, Azita Fathnezhad-Kazemi
    SAGE Open Medicine.2024;[Epub]     CrossRef
  • Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes
    Jingtong Huang, Andrea M. Yeung, David G. Armstrong, Ashley N. Battarbee, Jorge Cuadros, Juan C. Espinoza, Samantha Kleinberg, Nestoras Mathioudakis, Mark A. Swerdlow, David C. Klonoff
    Journal of Diabetes Science and Technology.2023; 17(1): 224.     CrossRef
  • A Machine Learning-Based Severity Prediction Tool for the Michigan Neuropathy Screening Instrument
    Fahmida Haque, Mamun B. I. Reaz, Muhammad E. H. Chowdhury, Mohd Ibrahim bin Shapiai, Rayaz A. Malik, Mohammed Alhatou, Syoji Kobashi, Iffat Ara, Sawal H. M. Ali, Ahmad A. A. Bakar, Mohammad Arif Sobhan Bhuiyan
    Diagnostics.2023; 13(2): 264.     CrossRef
  • Prevalence and grade of diabetic peripheral neuropathy among known diabetic patients in rural Uganda
    Dalton Kambale Munyambalu, Idania Hildago, Yves Tibamwenda Bafwa, Charles Abonga Lagoro, Franck Katembo Sikakulya, Bienfait Mumbere Vahwere, Ephraim Dafiewhare, Lazaro Martinez, Fardous Abeya Charles
    Frontiers in Clinical Diabetes and Healthcare.2023;[Epub]     CrossRef
  • Study on risk factors of diabetic peripheral neuropathy and establishment of a prediction model by machine learning
    Xiaoyang Lian, Juanzhi Qi, Mengqian Yuan, Xiaojie Li, Ming Wang, Gang Li, Tao Yang, Jingchen Zhong
    BMC Medical Informatics and Decision Making.2023;[Epub]     CrossRef
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    Georgios Baskozos, Andreas C. Themistocleous, Harry L. Hebert, Mathilde M. V. Pascal, Jishi John, Brian C. Callaghan, Helen Laycock, Yelena Granovsky, Geert Crombez, David Yarnitsky, Andrew S. C. Rice, Blair H. Smith, David L. H. Bennett
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    Marwa Elsaeed Elhefnawy, Siti Maisharah Sheikh Ghadzi, Sabariah Noor Harun
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    Jung Keun Hyun
    The Journal of Korean Diabetes.2022; 23(4): 238.     CrossRef
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    Mathieu Ravaut, Hamed Sadeghi, Kin Kwan Leung, Maksims Volkovs, Kathy Kornas, Vinyas Harish, Tristan Watson, Gary F. Lewis, Alanna Weisman, Tomi Poutanen, Laura Rosella
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    Fahmida Haque, Mamun Bin Ibne Reaz, Muhammad Enamul Hoque Chowdhury, Geetika Srivastava, Sawal Hamid Md Ali, Ahmad Ashrif A. Bakar, Mohammad Arif Sobhan Bhuiyan
    Diagnostics.2021; 11(5): 801.     CrossRef
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    Md. Maniruzzaman, Md. Merajul Islam, Md. Jahanur Rahman, Md. Al Mehedi Hasan, Jungpil Shin
    Diabetes & Metabolic Syndrome: Clinical Research & Reviews.2021; 15(5): 102263.     CrossRef
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    Dae Youp Shin, Bora Lee, Won Sang Yoo, Joo Won Park, Jung Keun Hyun
    Journal of Clinical Medicine.2021; 10(19): 4576.     CrossRef
  • T1DMicro: A Clinical Risk Calculator for Type 1 Diabetes Related Microvascular Complications
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    Journal of Family Medicine and Primary Care.2020; 9(3): 1470.     CrossRef
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    Negar Asaad Sajadi, Hossein Mahjub, Shiva Borzouei, Maryam Farhadian
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  • Identification of risk factors for patients with diabetes: diabetic polyneuropathy case study
    Oleg Metsker, Kirill Magoev, Alexey Yakovlev, Stanislav Yanishevskiy, Georgy Kopanitsa, Sergey Kovalchuk, Valeria V. Krzhizhanovskaya
    BMC Medical Informatics and Decision Making.2020;[Epub]     CrossRef
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Associations between diabetes self-management and microvascular complications in patients with type 2 diabetes
Fatemeh Mehravar, Mohammad Ali Mansournia, Kourosh Holakouie-Naieni, Ensie Nasli-Esfahani, Nasrin Mansournia, Amir Almasi-Hashiani
Epidemiol Health. 2016;38:e2016004.   Published online January 25, 2016
DOI: https://doi.org/10.4178/epih.e2016004
  • 19,333 View
  • 291 Download
  • 33 Web of Science
  • 24 Crossref
AbstractAbstract PDF
Abstract
OBJECTIVES
Diabetes is a major public health problem that is approaching epidemic proportions globally. Diabetes self-management can reduce complications and mortality in type 2 diabetic patients. The purpose of this study was to examine associations between diabetes self-management and microvascular complications in patients with type 2 diabetes.
METHODS
In this cross-sectional study, 562 Iranian patients older than 30 years of age with type 2 diabetes who received treatment at the Diabetes Research Center of the Endocrinology and Metabolism Research Institute of the Tehran University of Medical Sciences were identified. The participants were enrolled and completed questionnaires between January and April 2014. Patients’ diabetes self-management was assessed as an independent variable by using the Diabetes Self-Management Questionnaire translated into Persian. The outcomes were the microvascular complications of diabetes (retinopathy, nephropathy, and neuropathy), identified from the clinical records of each patient. A multiple logistic regression model was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) between diabetes self-management and the microvascular complications of type 2 diabetes, adjusting for potential confounders.
RESULTS
After adjusting for potential confounders, a significant association was found between the diabetes self-management sum scale and neuropathy (adjusted OR, 0.64; 95% CI, 0.45 to 0.92, p=0.01). Additionally, weak evidence was found of an association between the sum scale score of diabetes self-management and nephropathy (adjusted OR, 0.71; 95% CI, 0.47 to 1.05, p=0.09).
CONCLUSIONS
Among patients with type 2 diabetes, a lower diabetes self-management score was associated with higher rates of nephropathy and neuropathy.
Summary

Citations

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    BMJ Open.2023; 13(8): e074739.     CrossRef
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    Guiyue Ma, Haiyan Fang, Xiang Wang, Yahui Meng, Yu Zhu, Chuanying Zhang
    Medicine.2023; 102(48): e36185.     CrossRef
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    Pandan Enggarwati, Debie Dahlia, Riri Maria
    Journal of Public Health Research.2022; 11(2): jphr.2021.2734.     CrossRef
  • Indirect costs of non‐healing diabetic foot wounds in an African origin population in Barbados
    André R. Greenidge, Simon Naitram, Kim R. Quimby, Simon G. Anderson, R. Clive Landis
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