Knee Osteoarthritis Prediction Using Machine Learning

Osteoarthritis (OA) is a common disease and the fourth leading cause of disability worldwide. It is the most common type of arthritis and degenerative knee disease that causes pain and limitation of motion, independence, and quality of life for millions of people. It is the most common type of arthritis, with more than 30 million people in the United States and more than 10 million people in Europe, South America, Asia, and the Middle East. Although there is currently no known cure, several biological, pharmacological, and environmental risk factors are thought to play a role in the emergence and advancement of this illness. This increases medical costs and the associated financial burden. Current standard treatments for osteoarthritis, including both pharmacological and symptomatic treatment, have limited effect on function and pain. In this proposed study, we used machine learning to create a model that predicts the risk of knee osteoarthritis. Machine learning techniques such as LSTM and CNN were used to create the models. Our model predicted the risk of knee osteoarthritis with an accuracy of 74%. Using the CNN method with small updates, the accuracy of the proposed model is improved to 96%. These models can be used to identify people at high risk of disease and assist them in taking preventive measures. This result has prompted stakeholders and large organizations to develop accurate models to predict the risk of knee osteoarthritis.