This paper aims at estimating pathological subjects from a population through various physical information using genetic algorithm (GA). For comparison purposes, K-Means (KM) clustering algorithm has also been used for the estimation. Dataset consisting of some physical factors (age, weight, and height) and tibial rotation values was provided from the literature. Tibial rotation types are four groups as RTER, RTIR, LTER, and LTIR. Each tibial rotation group is divided into three types. Narrow (Type 1) and wide (Type 3) angular values were called pathological and normal (Type 2) angular values were called nonpathological. Physical information was used to examine if the tibial rotations of the subjects were pathological. Since the GA starts randomly and walks all solution space, the GA is seen to produce far better results than the KM for clustering and optimizing the tibial rotation data assessments with large number of subjects even though the KM algorithm has similar effect with the GA in clustering with a small number of subjects. These findings are discovered to be very useful for all health workers such as physiotherapists and orthopedists, in which this consequence is expected to help clinicians in organizing proper treatment programs for patients.
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.
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