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Publication Information
Affiliation
Department of Electrical, Electronic and System Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia.; Department of Electrical, Electronic and System Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia.; Department of Electrical Engineering, Qatar University, Doha 2713, Qatar.; Department of Physics and Electronics, Dr. Ram Manohar Lohia Avadh University, Ayodhya 224001, India.; Department of Electrical, Electronic and System Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia.; Department of Electrical, Electronic and System Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia.; Department Electrical and Electronic Engineering, Xiamen University Malaysia, Bandar Sunsuria, Sepang 43900, Selangor, Malaysia.
Authors
Haque Fahmida, Bin Ibne Reaz Mamun, Chowdhury Muhammad Enamul Hoque, Srivastava Geetika, Hamid Md Ali Sawal, Bakar Ahmad Ashrif A, Bhuiyan Mohammad Arif Sobhan
Studies
Abstract
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.