Machine learning approaches for early detection and management of musculoskeletal conditions
Pawan Whig, Ebtesam Shadadi, Shama Kouser, Lathifah Alamer
Abstract
Musculoskeletal conditions have a significant impact on quality of life.This study explores the use of machine learning algorithms for early detection and management of such conditions.Different models were evaluated using a dataset of musculoskeletal images and clinical information.Results demonstrate accurate classification with high sensitivity and specificity.A neural network was developed for detecting chronic lower back pain, achieving an impressive validation F1 score of 89%-93%.This highlights the potential of artificial intelligence in improving early detection and management.Future research should address data outliers to enhance model performance.Overall, neural networks are a valuable tool for early detection and management of musculoskeletal conditions, leading to improved patient outcomes.These findings suggest promising avenues for future research and implications for early detection and management in this field.