Abstract
Osteoarthritis is a joint disease that commonly occurs in the knee (KOA). The continuous increase in medical data regarding KOA has triggered researchers to incorporate artificial intelligence analytics for KOA prognosis or treatment. In this study, two approaches are presented to predict the progression of knee joint space narrowing (JSN) in each knee and in both knees combined. A machine learning approach is proposed with the use of multidisciplinary data from the osteoarthritis initiative database. The proposed methodology employs: (i) A clustering process to identify groups of people with progressing and non-progressing JSN; (ii) a robust feature selection (FS) process consisting of filter, wrapper, and embedded techniques that identifies the most informative risk factors; (iii) a decision making process based on the evaluation and comparison of various classification algorithms towards the selection and development of the final predictive model for JSN; and (iv) post-hoc interpretation of the features’ impact on the best performing model. The results showed that bounding the JSN progression of both knees can result to more robust prediction models with a higher accuracy (83.3%) and with fewer risk factors (29) compared to the right knee (77.7%, 88 risk factors) and the left knee (78.3%, 164 risk factors), separately.
Highlights
Osteoarthritis is the most common form of arthritis while the knee is the most frequently affected joint [1]
For the right leg, it can be observed that a percentage of the patients with low joint space narrowing (JSN) alterations were erroneously clustered in Cluster 1, which represents the patients with stable condition or without Knee osteoarthritis (KOA)
The main objective of this study was the accurate prediction of JSN in KOA patients based on a machine learning pipeline trained on multimodal data from the osteoarthritis initiative (OAI) (725 features in total were considered)
Summary
Osteoarthritis is the most common form of arthritis while the knee is the most frequently affected joint [1]. Knee osteoarthritis (KOA) is a chronic disease that can lead to joint damage, pain, stiffness, and loss of physical function. These physical limitations have a negative impact to the social life, mental health, and quality of life of KOA patients [1,2]. The increasing amount of medical data related to KOA permitted the development of more recent studies by using artificial intelligence and big data. Few studies in the literature have adopted advanced analytic techniques such as machine learning (ML) models, to predict the development of KOA [3,4]
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