• Saman Shahid, Aatir Javaid, Usman Amjad, and Jawad Rasheed.
• National University of Computer and Emerging Sciences, Foundation for the Advancement of Science and Technology, Department of Sciences and Humanities - Lahore, Pakistan.
• Rev Assoc Med Bras (1992). 2024 Jan 1; 70 (9): e20240381e20240381.

ObjectiveThe study used machine learning models to predict the clinical outcome with various attributes or when the models chose features based on their algorithms.MethodsPatients who presented to an orthopedic outpatient department with joint swelling or myalgia were included in the study. A proforma collected clinical information on age, gender, uric acid, C-reactive protein, and complete blood count/liver function test/renal function test parameters. Machine learning decision models (Random Forest and Gradient Boosted) were evaluated with the selected features/attributes. To categorize input data into outputs of indications of joint discomfort, multilayer perceptron and radial basis function-neural networks were used.ResultsThe random forest decision model outperformed with 97% accuracy and minimum errors to anticipate joint pain from input attributes. For predicted classifications, the multilayer perceptron fared better with an accuracy of 98% as compared to the radial basis function. Multilayer perceptron achieved the following normalized relevance: 100% (uric acid), 10.3% (creatinine), 9.8% (AST), 5.4% (lymphocytes), and 5% (C-reactive protein) for having joint pain. Uric acid has the highest normalized relevance for predicting joint pain.ConclusionThe earliest artificial intelligence-based detection of joint pain will aid in the prevention of more serious orthopedic complications.

27 keywords...

hide…