Applying a computational intelligence method to predict the rehabilitation treatment for females with lateral patellar displacement. Issue 1 (March 2018)

• Record Type: Journal Article
• Authors: Karimzadehfini, Atiye; Zolaktaf, Vahid; Mahdavinejad, Reza
• Is Part Of: Performance enhancement & health. Volume 6:Issue 1(2018)
• Page Start: 36
• Page End: 42
• Publication Date: 2018-03
• Subjects: Adaptive network-based fuzzy inference system -- Subtractive clustering method -- Fuzzy c-means clustering method -- Rehabilitation treatment -- Lateral patellar displacement; Doping in sports -- Periodicals; Athletic ability -- Periodicals; Sports medicine -- Periodicals; Performance-Enhancing Substances -- adverse effects -- Periodicals; Doping in Sports -- Periodicals; Athletic Performance -- Periodicals; Sports Medicine -- Periodicals; Athletic ability; Doping in sports; Sports medicine; Periodicals; 613.7
• DOI: 10.1016/j.peh.2017.09.001
• Languages: English
• ISSNs: 2211-2669

Abstract

Highlights: Computational intelligence methods are applied to aid physicians and other medical staff in making medical decisions. Computational intelligence methods may overcome traditional techniques, which are subjective and time-consuming. This study is intended to aid the practitioners in their decision making regarding to the best rehabilitation treatment while decreasing medical errors, improving healthcare, and saving physicians and patient time. The aim of our study was design and test computational intelligence methods (ANFIS-SCM and ANFIS-FCM models) using the demographic and clinical characteristics of patients to determine whether the ANFIS models could accurately predict the rehabilitation treatment prescribed by the physicians for females with LPD. Abstract: A lateral positioned patella has long been regarded as a major contributing factor in the development of patella femoral pain (PFP). Despite extensive research, there is still little consensus as to the most effective treatment strategy for the management of patients with lateral patellar displacement (LPD). Computational intelligence methods are proving useful aids to physicians and other medical staff, improving objectivity when making diagnostic and treatment decisions and reducing the time to make decisions. This study describes an adaptive network-based fuzzy inference system (ANFIS) used to build a model for the indirect prediction of rehabilitation treatment outcomes for females with LPD from only demographic and clinical characteristics. The prediction abilities offered using two ANFIS models are presented using data from 48 female patients referred to rehabilitation clinics of Isfahan Ayatollah Kashani and Al Zahra hospitals, Iran. The results indicate that the ANFIS model has strong potential to improve indirect prediction of rehabilitation treatment for females with LPD with a high degree of accuracy and robustness.