A survey on classification methodologies utilized for classifying the knee joint disorder levels using vibroarthrographic signals. (2023)

• Record Type: Journal Article
• Authors: Balajee, A.; Venkatesan, R.
• Is Part Of: Materials today. Volume 80:Part 3(2023)
• Page Start: 3240
• Page End: 3243
• Publication Date: 2023
• Subjects: Vibroarthrography (VAG) -- Classification -- Arthroscopy -- Support Vector Machines (SVM) -- Machine Learning -- Knee joint Pathology; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2021.07.219
• Languages: English
• ISSNs: 2214-7853

Abstract

Abstract: The largest as well as complicated joint in the human body is knee joint. A minor pathology in human knee can lead to major discomfort to a person. The pathology may be from a simple stress in knee joints to the higher effective levels of pathology that includes osteoarthritis (OA), chondromalacia patella (CMP) and so on. Hence prediction as well as classification of knee joint pathology plays a vital role for computational medical diagnosis. Pathology identification can be categorized in to two levels invasive and non – invasive methodologies. Invasive are purely medical as well as painful process which are less preferred for predictions. Noninvasive are clinical and computational ones that includes X-Rays, Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Vibroarthography (VAG) etc. This paper compares various classification methodologies utilized by Vibroarthographic (VAG) signal based knee joint disorder level identification and performs a binary classification of data samples as a survey.