Orthopedic disease classification based on breadth-first search algorithm

• Published in: Scientific reports 2024-10, Vol.14 (1), p.23368-18, Article 23368
• Main Authors: Elshewey, Ahmed M., Osman, Ahmed M.
• Format: Article
• Language: English
• Subjects: 631/114; 639/705; Accuracy; Algorithms; Healthcare; Humanities and Social Sciences; Humans; Hybrid RF model, BFS-RF; Machine Learning; multidisciplinary; Musculoskeletal Diseases - classification; Musculoskeletal Diseases - diagnosis; Musculoskeletal system; Optimization; Orthopedic disease classification; Orthopedics; Science; Science (multidisciplinary); Sensitivity analysis
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-73559-6

Orthopedic diseases are widespread worldwide, impacting the body’s musculoskeletal system, particularly those involving bones or hips. They have the potential to cause discomfort and impair functionality. This paper aims to address the lack of supplementary diagnostics in orthopedics and improve the method of diagnosing orthopedic diseases. The study uses binary breadth-first search (BBFS), binary particle swarm optimization (BPSO), binary grey wolf optimizer (BGWO), and binary whale optimization algorithm (BWAO) for feature selections, and the BBFS makes an average error of 47.29% less than others. Then we apply six machine learning models, i.e., RF, SGD, NBC, DC, QDA, and ET. The dataset used contains 310 instances and six distinct features. Through experimentation, the RF model led to optimal outcomes during comparison to the remaining models, with an accuracy of 91.4%. The parameters of the RF model were optimized using four optimization algorithms: BFS, PSO, WAO, and GWO. To check how well the optimized RF works on the dataset, this paper uses prediction evaluation metrics such as accuracy, sensitivity, specificity, F-score, and the AUC curve. The results showed that the BFS-RF can improve the performance of the original classifier compared with others with 99.41% accuracy.