Prediction of Joint Space Narrowing Progression in Knee Osteoarthritis Patients
1
Department of Computer Science and Biomedical Informatics, University of Thessaly, 35131 Lamia, Greece
2
Institute for Bio-Economy & Agri-Technology, Center for Research and Technology Hellas, 38333 Volos, Greece
3
Department of Physical Education & Sport Science, University of Thessaly, 42100 Trikala, Greece
4
AIDEAS OÜ, Narva mnt 5, 10117 Harju Maakond, Estonia
*
Author to whom correspondence should be addressed.
Academic Editor: Sameer Antani
Diagnostics 2021, 11(2), 285; https://doi.org/10.3390/diagnostics11020285
Received: 31 December 2020
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Revised: 3 February 2021
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Accepted: 9 February 2021
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Published: 11 February 2021
(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)
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.
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Keywords:
machine learning; knee osteoarthritis; joint space narrowing prediction; feature selection; interpretation
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MDPI and ACS Style
Ntakolia, C.; Kokkotis, C.; Moustakidis, S.; Tsaopoulos, D. Prediction of Joint Space Narrowing Progression in Knee Osteoarthritis Patients. Diagnostics 2021, 11, 285. https://doi.org/10.3390/diagnostics11020285
AMA Style
Ntakolia C, Kokkotis C, Moustakidis S, Tsaopoulos D. Prediction of Joint Space Narrowing Progression in Knee Osteoarthritis Patients. Diagnostics. 2021; 11(2):285. https://doi.org/10.3390/diagnostics11020285
Chicago/Turabian StyleNtakolia, Charis, Christos Kokkotis, Serafeim Moustakidis, and Dimitrios Tsaopoulos. 2021. "Prediction of Joint Space Narrowing Progression in Knee Osteoarthritis Patients" Diagnostics 11, no. 2: 285. https://doi.org/10.3390/diagnostics11020285
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