Predicting Knee Cartilage Degradation and Osteoarthritis Onset Using a Hybrid Mathematical Modeling and Machine Learning Framework
Osteoarthritis (OA) is the most prevalent form of arthritis, commonly affecting the knee joint and characterized by the progressive degeneration of articular cartilage (AC). Among the various contributing factors, repetitive cyclic loading plays a significant role in accelerating this deterioration....
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11039623/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849424854096281600 |
|---|---|
| author | F. Mekrane R. Ouladsine A. Barkaoui R. Ghandour |
| author_facet | F. Mekrane R. Ouladsine A. Barkaoui R. Ghandour |
| author_sort | F. Mekrane |
| collection | DOAJ |
| description | Osteoarthritis (OA) is the most prevalent form of arthritis, commonly affecting the knee joint and characterized by the progressive degeneration of articular cartilage (AC). Among the various contributing factors, repetitive cyclic loading plays a significant role in accelerating this deterioration. Knee osteoarthritis (KOA), in particular, represents a big data challenge due to the complexity, heterogeneity, and large volume of data required for its analysis and prediction. To address this, we employed a validated mathematical model capable of predicting the number of remaining mechanical cycles that the AC can endure during daily walking before showing signs of degradation. This model facilitated the generation of a wide range of simulations and scenarios that incorporated diverse individual profiles, including age, height, weight, gender, walking duration, and accumulated cartilage damage, along with their corresponding remaining mechanical cycles. The objectives of this study are twofold: (i) to use the degradation model’s outputs to train, validate, and test four machine learning (ML) models, and (ii) to compare the best-performing ML model with a Long Short-Term Memory (LSTM) neural network. Among the models tested, the Support Vector Regressor (SVR) demonstrated superior predictive performance, achieving an R2 of 0.95, a Root Mean Squared Error (RMSE) of 0.13, and a Mean Absolute Percentage Error (MAPE) of 2.5%. These findings provide a solid foundation for accurately predicting knee cartilage damage and guiding the prescription of personalized treatment strategies aimed at delaying or preventing the onset of KOA. |
| format | Article |
| id | doaj-art-e6a1b248ebfd44878790e0adbec40768 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-e6a1b248ebfd44878790e0adbec407682025-08-20T03:29:58ZengIEEEIEEE Access2169-35362025-01-011310784410785510.1109/ACCESS.2025.358107111039623Predicting Knee Cartilage Degradation and Osteoarthritis Onset Using a Hybrid Mathematical Modeling and Machine Learning FrameworkF. Mekrane0https://orcid.org/0009-0006-1167-8409R. Ouladsine1A. Barkaoui2https://orcid.org/0000-0001-7268-5860R. Ghandour3https://orcid.org/0000-0002-1917-644XLERMA Laboratory, College of Engineering and Architecture, International University of Rabat, Sala Al Jadida, MoroccoLERMA Laboratory, College of Engineering and Architecture, International University of Rabat, Sala Al Jadida, MoroccoLERMA Laboratory, College of Engineering and Architecture, International University of Rabat, Sala Al Jadida, MoroccoCollege of Engineering and Technology, American University of the Middle East, Kuwait, Egaila, KuwaitOsteoarthritis (OA) is the most prevalent form of arthritis, commonly affecting the knee joint and characterized by the progressive degeneration of articular cartilage (AC). Among the various contributing factors, repetitive cyclic loading plays a significant role in accelerating this deterioration. Knee osteoarthritis (KOA), in particular, represents a big data challenge due to the complexity, heterogeneity, and large volume of data required for its analysis and prediction. To address this, we employed a validated mathematical model capable of predicting the number of remaining mechanical cycles that the AC can endure during daily walking before showing signs of degradation. This model facilitated the generation of a wide range of simulations and scenarios that incorporated diverse individual profiles, including age, height, weight, gender, walking duration, and accumulated cartilage damage, along with their corresponding remaining mechanical cycles. The objectives of this study are twofold: (i) to use the degradation model’s outputs to train, validate, and test four machine learning (ML) models, and (ii) to compare the best-performing ML model with a Long Short-Term Memory (LSTM) neural network. Among the models tested, the Support Vector Regressor (SVR) demonstrated superior predictive performance, achieving an R2 of 0.95, a Root Mean Squared Error (RMSE) of 0.13, and a Mean Absolute Percentage Error (MAPE) of 2.5%. These findings provide a solid foundation for accurately predicting knee cartilage damage and guiding the prescription of personalized treatment strategies aimed at delaying or preventing the onset of KOA.https://ieeexplore.ieee.org/document/11039623/Articular cartilagecyclic loadingknee osteoarthritismachine learningprognostics |
| spellingShingle | F. Mekrane R. Ouladsine A. Barkaoui R. Ghandour Predicting Knee Cartilage Degradation and Osteoarthritis Onset Using a Hybrid Mathematical Modeling and Machine Learning Framework IEEE Access Articular cartilage cyclic loading knee osteoarthritis machine learning prognostics |
| title | Predicting Knee Cartilage Degradation and Osteoarthritis Onset Using a Hybrid Mathematical Modeling and Machine Learning Framework |
| title_full | Predicting Knee Cartilage Degradation and Osteoarthritis Onset Using a Hybrid Mathematical Modeling and Machine Learning Framework |
| title_fullStr | Predicting Knee Cartilage Degradation and Osteoarthritis Onset Using a Hybrid Mathematical Modeling and Machine Learning Framework |
| title_full_unstemmed | Predicting Knee Cartilage Degradation and Osteoarthritis Onset Using a Hybrid Mathematical Modeling and Machine Learning Framework |
| title_short | Predicting Knee Cartilage Degradation and Osteoarthritis Onset Using a Hybrid Mathematical Modeling and Machine Learning Framework |
| title_sort | predicting knee cartilage degradation and osteoarthritis onset using a hybrid mathematical modeling and machine learning framework |
| topic | Articular cartilage cyclic loading knee osteoarthritis machine learning prognostics |
| url | https://ieeexplore.ieee.org/document/11039623/ |
| work_keys_str_mv | AT fmekrane predictingkneecartilagedegradationandosteoarthritisonsetusingahybridmathematicalmodelingandmachinelearningframework AT rouladsine predictingkneecartilagedegradationandosteoarthritisonsetusingahybridmathematicalmodelingandmachinelearningframework AT abarkaoui predictingkneecartilagedegradationandosteoarthritisonsetusingahybridmathematicalmodelingandmachinelearningframework AT rghandour predictingkneecartilagedegradationandosteoarthritisonsetusingahybridmathematicalmodelingandmachinelearningframework |