Paper Key : IRJ************354
Author: Poojitha S
Date Published: 01 Jul 2024
Abstract
Osteoarthritis of the knee is a common degenerative joint disease that causes discomfort and impairs movement. This highlights the significance of early diagnosis and prognosis for the best possible patient care and successful therapies. This research delves into utilizing both clinical records and radiographic data to forecast knee- osteoarthritis through advanced deep learning algorithms, including neural-networks. The dataset includes a wide range of imaging results, medical histories, and demographic information from a heterogeneous group of people. Metrics like AUC-ROC, recall, accuracy, precision, and F1-score are used to evaluate the models; neural networks are shown to be the most reliable and accurate. Results indicates that deep-learning algorithms hold promise in efficiently predicting knee osteoarthritis, thereby assisting healthcare providers in decision-making processes and potentially paving the way for personalized treatment strategies. Future endeavors will concentrate on incorporating larger datasets and developing real-time prediction systems to enhance clinical applicability.Keywords: Knee osteoarthritis, machine learning, prediction models, clinical data, radiographic data, early diagnosis.