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Prediction of Pathological Subjects Using Genetic Algorithms

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 hei...

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Detalles Bibliográficos
Autores principales: Sari, Murat, Tuna, Can
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5829316/
https://www.ncbi.nlm.nih.gov/pubmed/29623101
http://dx.doi.org/10.1155/2018/6154025
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author Sari, Murat
Tuna, Can
author_facet Sari, Murat
Tuna, Can
author_sort Sari, Murat
collection PubMed
description 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.
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spelling pubmed-58293162018-04-05 Prediction of Pathological Subjects Using Genetic Algorithms Sari, Murat Tuna, Can Comput Math Methods Med Research Article 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. Hindawi 2018-01-29 /pmc/articles/PMC5829316/ /pubmed/29623101 http://dx.doi.org/10.1155/2018/6154025 Text en Copyright © 2018 Murat Sari and Can Tuna. https://creativecommons.org/licenses/by/4.0/ 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.
spellingShingle Research Article
Sari, Murat
Tuna, Can
Prediction of Pathological Subjects Using Genetic Algorithms
title Prediction of Pathological Subjects Using Genetic Algorithms
title_full Prediction of Pathological Subjects Using Genetic Algorithms
title_fullStr Prediction of Pathological Subjects Using Genetic Algorithms
title_full_unstemmed Prediction of Pathological Subjects Using Genetic Algorithms
title_short Prediction of Pathological Subjects Using Genetic Algorithms
title_sort prediction of pathological subjects using genetic algorithms
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5829316/
https://www.ncbi.nlm.nih.gov/pubmed/29623101
http://dx.doi.org/10.1155/2018/6154025
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