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Osteoarthritis Disease Prediction Based on Machine Learning Techniques
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Author(s): V. Sathya (SRM Institute of Science and Technology, India), Shalini Parthiban (Panimalar Engineering College, India), M. Megavarshini (Panimalar Engineering College, India), V. Shenbagaraman (SRM Institute of Science and Technology, India)and R. Ramya (Panimalar Engineering College, India)
Copyright: 2024
Pages: 13
Source title:
Enhancing Medical Imaging with Emerging Technologies
Source Author(s)/Editor(s): Avinash Kumar Sharma (Sharda University, India), Nitin Chanderwal (University of Cincinnati, USA), Shobhit Tyagi (Sharda University, India), Prashant Upadhyay (Sharda University, India)and Amit Kumar Tyagi (National Institute of Fashion Technology, New Delhi, India)
DOI: 10.4018/979-8-3693-5261-8.ch006
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Abstract
The most typical type of arthritis is osteoarthritis. Osteoarthritis is a degenerative joint disease affecting millions of people worldwide. It usually affects the hands, hips, and knees. People with osteoarthritis struggle to perform simple tasks such as walking, standing, or climbing stairs. In osteoarthritis, a joint's cartilage starts to degrade, and the underlying bone starts to alter. One of the main causes of disability and a prevalent disease of the elderly population is osteoarthritis. Moreover, because of the persistent pain and impairment brought on by the condition, osteoarthritis can also result in psychological distress, such as sorrow and anxiety. The advanced deep learning-based convolutional neural network and several machine learning-based techniques are applied in comparison. Using the random forest method, this chapter divides osteoarthritis disease into four categories of severity: Grade-0, Grade-1, Grade-2, Grade-3, Grade-4. Extensive experiments demonstrate that the proposed model outperformed with a 99% accuracy score for predicting osteoarthritis.
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