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Computational and Mathematical Methods in Medicine
Volume 2018, Article ID 6154025, 9 pages
Research Article

Prediction of Pathological Subjects Using Genetic Algorithms

Department of Mathematics, Yildiz Technical University, Esenler, Istanbul 34220, Turkey

Correspondence should be addressed to Murat Sari; rt.ude.zidliy@miras

Received 29 August 2017; Revised 13 December 2017; Accepted 2 January 2018; Published 29 January 2018

Academic Editor: Thierry Busso

Copyright © 2018 Murat Sari and Can Tuna. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper aims at estimating pathological subjects from a population through various physical information using genetic algorithm (GA). For comparison purposes, -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.