Osteoarthritis (OA) is characterized by the degradation of the layer between the joints. Osteoarthritis (OA) is a situation that results from deformation of the layer between two bones. Pain where the bone joins Osteoarthritis affects all patients. It mostly harms the cartilage in the joints, which fallouts in stiffness, discomfort, and edema. It is the main cause of soreness and debility. It is anticipated that as the people ages and obesity rates rise, the occurrence of OA would gradually rise as well[1]. Pain while activity or movement in elderly people individuals can notably disrupts their daily activities, reducing ability to function alone. Common causes may due to arthritis, muscle weakness, and joint degeneration. Managing pain through proper medical treatment can restore their daily comfort for osteoarthritis patients One of the main musculoskeletal conditions that contribute to years of incapacity is osteoarthritis. Since osteoarthritis is more dominant in older adults (nearly 70% of those over 55), its frequency is predictable to rise as the world's population ages. Athletes and those who have experienced joint stress or injury may be at risk for osteoarthritis, even though it typically first manifests in the late 40s to mid-50s. About 60% of people with osteoarthritis are women [5]. Classification shows an essential part in diagnosing joint degeneration by measuring the severity using algorithms designed for machine learning. This helps to design optimal treatment plans. Proper diagnosis improves mobility and quality of life. So, classification is plays important part in diagnosing Osteoarthritis in patients. Numerous machine learning methods are available for the analysis and ordering of osteoarthritis. Multiple machine learning techniques gives different results. In this article combinational machine learning techniques are applied. Combined study may be beneficial for getting more accurate results in medical applications working on Osteoarthritis detection. This paper represents a combination of machine learning based approach for binary classification. Features are mined using Histogram of Oriented Gradients and Gray Level Co-occurrence Matrix is united together in the CSV format, which was then passed as input for machine learning classification models. Results demonstrate advantageous outcomes with high accuracy and significant potential for medical application.
Osteoarthritis, Machine Learning, Classification, GLCM, Knee Osteoarthritis, HOG, Feature Extraction, Hybrid Machine Learning model, Medical imaging
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