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AuthorHaque, Fahmida
AuthorReaz, Mamun B. I.
AuthorChowdhury, Muhammad E. H.
AuthorHashim, Fazida H.
AuthorArsad, Norhana
AuthorAli, Sawal H. M.
Available date2023-04-17T06:57:47Z
Publication Date2021
Publication NameIEEE Access
ResourceScopus
URIhttp://dx.doi.org/10.1109/ACCESS.2020.3048742
URIhttp://hdl.handle.net/10576/41992
AbstractDiabetic sensorimotor polyneuropathy (DSPN) is an early indicator for non-healing diabetic wounds and diabetic foot ulcers, which account for one of the most common complications of diabetes, leading to increased healthcare cost, decreased quality of life, infections, amputations, and death. Early detection and intelligent classification tools for DSPN can allow correct diagnosis and treatment of painful diabetic neuropathy as well as a timely intervention to prevent foot ulceration, amputation, and other diabetic complications. Hence, to successfully mitigate the prevalence of DSPN, this study aims to depict an intelligent DSPN severity classifier using Adaptive Neuro Fuzzy Inference System (ANFIS). Michigan Neuropathy Screening Instrumentation (MNSI) was considered as the input for identification and stratification of DSPN. Patients have been classified into four classes: Absent, Mild, Moderate, and Severe. The model accuracy was validated with the results from different machine learning algorithms. The Accuracy, sensitivity, and specificity of the ANFIS model are 91.17±1.18%, 92±2.26%, 96.72±0.93%, respectively. The proposed classifier was used to classify the Epidemiology of Diabetes Interventions and Complications (EDIC) clinical trial patients and observed that in the first, eighth, and nineteenth EDIC years 18.31%, 39.45%, and 59.14% patients had different levels of DSPN. This study also investigates the changes in muscle activity during gait from three different lower limb muscles (vastus lateralis (VL), tibialis anterior (TA), and gastrocnemius medialis (GM)) electromyography (EMG) of DSPN patients with different severity levels classified by the proposed classifier and observed that VL and GM muscles show an increase in delay for activation peak and decrease in peak magnitude during gait with the progression of DSPN severity. Based on this observation, the ANFIS model was trained using the extracted EMG features for DSPN severity stratification and showed ...
SponsorCorresponding authors: Muhammad E. H. Chowdhury (mchowdhury@qu.edu.qa), Fazida H. Hashim (fazida@ukm.edu.my), and Mamun B. I. Reaz (mamun@ukm.edu.my) This work was supported in part by the Universiti Kebangsaan Malaysia (UKM) Research University Grant DIP-2020-004 and Grant GUP-2019-010, and in part by the Qatar National Research Foundation (QNRF) under Grant NPRP12s-0227-190164. Open Access funding provided by the Qatar National Library.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectANFIS
classifier
diabetic neuropathy
DSPN
fuzzy system
TitleDiabetic Sensorimotor Polyneuropathy Severity Classification Using Adaptive Neuro Fuzzy Inference System
TypeArticle
Pagination7618-7631
Volume Number9


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