A Novel Fuzzy Expert System for the Identification of Severity of Carpal Tunnel Syndrome

Author:

Kunhimangalam Reeda1ORCID,Ovallath Sujith2,Joseph Paul K.1

Affiliation:

1. National Institute of Technology, Calicut (NITC), Kozhikode, Kerala 673601, India

2. Department of Neurology, Kannur Medical College, Anjarakandy, Kannur, Kerala 670612, India

Abstract

The diagnosis of carpal tunnel syndrome, a peripheral nerve disorder, at the earliest possible stage is very crucial because if left untreated it may cause permanent nerve damage reducing the chances of successful treatment. Here a novel Fuzzy Expert System designed using MATLAB is proposed for identification of severity of CTS. The data used were the nerve conduction study data obtained from Kannur Medical College, India. It consists of thirteen input fields, which include the clinical values of the diagnostic test and the clinical symptoms, and the output field gives the disease severity. The results obtained match with the expert’s opinion with 98.4% accuracy and high degrees of sensitivity and specificity. Since quantification of the intensity of CTS is a crucial step in the electrodiagnostic procedure and is important for defining prognosis and therapeutic measures, such an expert system can be of immense use in those regions where the service of such specialists may not be readily available. It may also prove useful in combination with other systems in providing diagnostic and predictive medical opinions and can add value if introduced into the routine clinical consultations to arrive at the most accurate medical diagnosis in a timely manner.

Publisher

Hindawi Limited

Subject

General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine

Cited by 14 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Application of Machine Learning Algorithms for Carpal Tunnel Syndrome;Journal of the Anatomical Society of India;2024-04

2. Sistema experto difuso para la calificación preliminar de proyectos de captura y almacenamiento de CO2;Revista Internacional de Contaminación Ambiental;2023-09-07

3. Design of a fuzzy input expert system visual information interface for classification of apnea and hypopnea;Multimedia Tools and Applications;2023-07-27

4. ARTIFICIAL INTELLIGENCE BASED RATING OF CARPAL TUNNEL SYNDROME EFFICACY IN CLINICAL DIAGNOSIS;Acta Medica Nicomedia;2023-06-30

5. Artificial intelligence–assisted headache classification: a review;Augmenting Neurological Disorder Prediction and Rehabilitation Using Artificial Intelligence;2022

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