An Overview of Machine Learning Techniques for MRI-Based Knee Osteoarthritis Progression Prediction

An Overview of Machine Learning Techniques for MRI-Based Knee Osteoarthritis Progression Prediction

Shradha Chakor1, Pranali Chaskar, 2, Anjali Gite3, Aarti Nannaware4
1,2,3,4,, Department of Information Technology, Matoshri Aasarabai Polytechnic Eklahare Nashik 5Ms.M.P.Deshmukh Lecturer of Information Technology, Matoshri Aasarabai Polytechnic Eklahare Nashik 6Mr.M.P.Bhandakkar Head of Information Technology, Matoshri Aasarabai Polytechnic Eklahare Nashik

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Abstract - One common degenerative joint condition that has a substantial negative influence on quality of life and is placing an increasing strain on healthcare systems throughout the globe is knee osteoarthritis (OA). Even while MRI is essential for OA diagnosis and monitoring, conventional techniques that depend on manual cartilage segmentation are labour-intensive and subject to error. In order to overcome these obstacles, this work uses the cutting-edge Cartilage Damage Index (CDI) to present a unique    machine    learning-based     method     for     OA progression prediction using MRI data. More sensitive evaluations of cartilage changes over time are made possible by the CDI, which measures the thickness and structural integrity of cartilage at 36 distinct places inside the tibiofemoral    cartilagecompartment. Principal Component Analysis (PCA) is used to improve prediction accuracy, increase computing efficiency, and reduce dimensionality while maintaining important information. This study evaluates four machine learning algorithms ANN, SVM, Naïve Bayes & Random Forest for its efficacy in predicting the progression of osteoarthritis (OA) based on data obtained from CDI. Validated prediction findings utilise established clinical markers such as JSN in the medial and lateral compartments and abnormalities in KL grades. The findings indicate that the medial cartilage feature set had superior predictive capacity, with the highest overall accuracy achieved with the integration of medial and lateral features. This work provides a robust basis for the early diagnosis and monitoring of OA progression   by integrating     advanced  data    mining techniques   wi     machine  learning,   allowing   more personalised and effective treatment strategies in clinical practice.

Key Words: Cartilage Damage Index, Machine learning, Knee osteoarthritis, MRI-based prediction.