Research Article Performance Analysis of Conventional Machine Learning Algorithms for Diabetic Sensorimotor Polyneuropathy Severity Classification Using Nerve Conduction Studies Fahmida Haque, 1 Mamun B. I. Reaz , 1 Muhammad E. H. Chowdhury , 2 Serkan Kiranyaz, 2 Sawal H. M. Ali, 1 Mohammed Alhatou, 3,4 Rumana Habib, 5 Ahmad A. A. Bakar, 1 Norhana Arsad, 1 and Geetika Srivastava 6 1 Department of Electrical, Electronic and System Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia 2 Department of Electrical Engineering, Qatar University, Doha 2713, Qatar 3 Neuromuscular Division, Hamad General Hospital, Doha 3050, Qatar 4 Department of Neurology, Al khor Hospital, Doha 3050, Qatar 5 Department of Neurology, BIRDEM General Hospital, Dhaka-1000, Bangladesh 6 Department of Physics and Electronics, Dr. Ram Manohar Lohia Avadh University, Ayodhya 224001, India Correspondence should be addressed to Mamun B. I. Reaz; mamun@ukm.edu.my and Muhammad E. H. Chowdhury; mchowdhury@qu.edu.qa Received 9 December 2021; Revised 14 February 2022; Accepted 18 March 2022; Published 25 April 2022 Academic Editor: Paolo Crippa Copyright © 2022 Fahmida Haque et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background. Diabetic sensorimotor polyneuropathy (DSPN) is a major form of complication that arises in long-term diabetic patients. Even though the application of machine learning (ML) in disease diagnosis is very common and well-established in the field of research, its application in DSPN diagnosis using nerve conduction studies (NCS), is very limited in the existing literature. Method. In this study, the NCS data were collected from the Diabetes Control and Complications Trial (DCCT) and its follow-up Epidemiology of Diabetes Interventions and Complications (EDIC) clinical trials. e NCS variables are median motor velocity (m/sec), median motor amplitude (mV), median motor F-wave (msec), median sensory velocity (m/sec), median sensory amplitude (μV), Peroneal Motor Velocity (m/sec), peroneal motor amplitude (mv), peroneal motor F-wave (msec), sural sensory velocity (m/sec), and sural sensory amplitude (μV). ree different feature ranking techniques were used to analyze the per- formance of eight different conventional classifiers. Results. e ensemble classifier outperformed other classifiers for the NCS data ranked when all the NCS features were used and provided an accuracy of 93.40%, sensitivity of 91.77%, and specificity of 98.44%. e random forest model exhibited the second-best performance using all the ten features with an accuracy of 93.26%, sensitivity of 91.95%, and specificity of 98.95%. Both ensemble and random forest showed the kappa value 0.82, which indicates that the models are in good agreement with the data and the variables used and are accurate to identify DSPN using these ML models. Conclusion. is study suggests that the ensemble classifier using all the ten NCS variables can predict the DSPN severity which can enhance the management of DSPN patients. 1. Introduction Diabetic sensorimotor polyneuropathy (DSPN) is one of the major complications with a prevalence of 50% that arise in patients with long-term Diabetes mellitus (DM) [1–3]. DSPN is a type of nerve damage, which can lead to many lower limb complications such as numbness, burning, pinprick sensation, and pain. In the worst case for long term DSPN, it can lead to ulceration, and amputation, sugges- tively increasing the chance of early death and reducing the quality of life of DM patients [4–7]. About 40 to 60 million DM patients are affected with lower limb complications because of DSPN and in every 30 seconds, one lower limb is being amputated due to DSPN [8]. Understanding the Hindawi Computational Intelligence and Neuroscience Volume 2022, Article ID 9690940, 13 pages https://doi.org/10.1155/2022/9690940