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Open AccessArticle

Prediction of Tibial Rotation Pathologies Using Particle Swarm Optimization and K-Means Algorithms

1
Department of Mathematics, Yildiz Technical University, Istanbul 34220, Turkey
2
Department of Statistics, Yildiz Technical University, Istanbul 34220, Turkey
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2018, 7(4), 65; https://doi.org/10.3390/jcm7040065
Received: 24 January 2018 / Revised: 22 March 2018 / Accepted: 22 March 2018 / Published: 28 March 2018
(This article belongs to the Section Nuclear Medicine & Radiology)
The aim of this article is to investigate pathological subjects from a population through different physical factors. To achieve this, particle swarm optimization (PSO) and K-means (KM) clustering algorithms have been combined (PSO-KM). Datasets provided by the literature were divided into three clusters based on age and weight parameters and each one of right tibial external rotation (RTER), right tibial internal rotation (RTIR), left tibial external rotation (LTER), and left tibial internal rotation (LTIR) values were divided into three types as Type 1, Type 2 and Type 3 (Type 2 is non-pathological (normal) and the other two types are pathological (abnormal)), respectively. The rotation values of every subject in any cluster were noted. Then the algorithm was run and the produced values were also considered. The values of the produced algorithm, the PSO-KM, have been compared with the real values. The hybrid PSO-KM algorithm has been very successful on the optimal clustering of the tibial rotation types through the physical criteria. In this investigation, Type 2 (pathological subjects) is of especially high predictability and the PSO-KM algorithm has been very successful as an operation system for clustering and optimizing the tibial motion data assessments. These research findings are expected to be very useful for health providers, such as physiotherapists, orthopedists, and so on, in which this consequence may help clinicians to appropriately designing proper treatment schedules for patients. View Full-Text
Keywords: tibial rotation pathology; K-means clustering; particle swarm optimization tibial rotation pathology; K-means clustering; particle swarm optimization
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MDPI and ACS Style

Sari, M.; Tuna, C.; Akogul, S. Prediction of Tibial Rotation Pathologies Using Particle Swarm Optimization and K-Means Algorithms. J. Clin. Med. 2018, 7, 65.

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