• Advait Chandorkar
• Anjali Gupta
• Shraddha Yadav
• Suraj Sahani
• Sulbha S Yadav

Knee arthritis is not a rare condition, as it can occur to anyone. There are two types of knee arthritis: osteoarthritis and rheumatoid arthritis. Osteoarthritis is the most common condition to occur in anyone. This project focuses on the development of an advanced machine learning solution for addressing the challenges posed by knee osteoarthritis (OA), a prevalent and debilitating joint disorder. Leveraging the power of machine learning and deep learning techniques, the project aims to revolutionize early detection, prognosis, and personalized treatment recommendations for knee OA. Knee OA is a complex condition influenced by multifaceted factors such as genetics, lifestyle, and biomechanics. Conventional diagnostic methods often fall short of providing accurate predictions and personalized treatment strategies, leading to suboptimal patient outcomes. In this context, the proposed solution harnesses a diverse range of data sources, including clinical records, medical imaging scans, patient demographics, and lifestyle information. The project's methodology involves collecting and curating a comprehensive dataset comprising clinical records, imaging data, and patient-specific attributes. Cutting-edge machine learning and deep learning algorithms will then be employed to build predictive models capable of accurately diagnosing knee OA, estimating disease progression, and suggesting optimal treatment approaches. Rigorous evaluation and validation of the models will be conducted using appropriate metrics and techniques.

"A Study and Comparative Analysis of Machine Learning Approaches for Knee Osteoarthritis Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 11, page no.a737-a745, November-2023, Available :http://www.jetir.org/papers/JETIR2311092.pdf

"A Study and Comparative Analysis of Machine Learning Approaches for Knee Osteoarthritis Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 11, page no. ppa737-a745, November-2023, Available at : http://www.jetir.org/papers/JETIR2311092.pdf