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AuthorHaque, Fahmida
AuthorBin Ibne Reaz, Mamun
AuthorChowdhury, Muhammad E.
AuthorSrivastava, Geetika
AuthorHamid Md Ali, Sawal
AuthorBakar, Ahmad Ashrif A.
AuthorBhuiyan, Mohammad A.
Available date2023-04-17T06:57:46Z
Publication Date2021
Publication NameDiagnostics
ResourceScopus
URIhttp://dx.doi.org/10.3390/diagnostics11050801
URIhttp://hdl.handle.net/10576/41982
AbstractBackground: Diabetic peripheral neuropathy (DSPN), a major form of diabetic neuropathy, is a complication that arises in long-term diabetic patients. Even though the application of machine learning (ML) in disease diagnosis is a very common and well-established field of research, its application in diabetic peripheral neuropathy (DSPN) diagnosis using composite scoring techniques like Michigan Neuropathy Screening Instrumentation (MNSI), is very limited in the existing literature. Method: In this study, the MNSI data were collected from the Epidemiology of Diabetes Interventions and Complications (EDIC) clinical trials. Two different datasets with different MNSI variable combinations based on the results from the eXtreme Gradient Boosting feature ranking technique were used to analyze the performance of eight different conventional ML algorithms. Results: The random forest (RF) classifier outperformed other ML models for both datasets. However, all ML models showed almost perfect reliability based on Kappa statistics and a high correlation between the predicted output and actual class of the EDIC patients when all six MNSI variables were considered as inputs. Conclusions: This study suggests that the RF algorithm-based classifier using all MNSI variables can help to predict the DSPN severity which will help to enhance the medical facilities for diabetic patients. 2021 by the authors. Licensee MDPI, Basel, Switzerland.
SponsorFunding: This research is financially supported by Xiamen University Malaysia, Project number XMUMRF/2018-C2/IECE/0002, Universiti Kebangsaan Malaysia (UKM), grant nos. DPK-2021-001, GP-2019-K017701, and DIP-2020-004; and Qatar National Research Foundation (QNRF), grant no. NPRP12s-0227-190164.
Languageen
PublisherMDPI
SubjectDiabetic neuropathy
DSPN
Machine learning
ML
MNSI
Severity classification
TitlePerformance analysis of conventional machine learning algorithms for diabetic sensorimotor polyneuropathy severity classification
TypeArticle
Issue Number5
Volume Number11
dc.accessType Open Access


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