Keratoconus Severity Classification Using Features Selection and Machine Learning Algorithms

Keratoconus is a noninflammatory disease characterized by thinning and bulging of the cornea, generally appearing during adolescence and slowly progressing, causing vision impairment. However, the detection of keratoconus remains difficult in the early stages of the disease because the patient does...

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Autores principales:	Mustapha Aatila, Mohamed Lachgar, Hrimech Hamid, Ali Kartit
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/100d10b553f941eaabe16c544496b151
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spelling	oai:doaj.org-article:100d10b553f941eaabe16c544496b1512021-11-29T00:55:28ZKeratoconus Severity Classification Using Features Selection and Machine Learning Algorithms1748-671810.1155/2021/9979560https://doaj.org/article/100d10b553f941eaabe16c544496b1512021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/9979560https://doaj.org/toc/1748-6718Keratoconus is a noninflammatory disease characterized by thinning and bulging of the cornea, generally appearing during adolescence and slowly progressing, causing vision impairment. However, the detection of keratoconus remains difficult in the early stages of the disease because the patient does not feel any pain. Therefore, the development of a method for detecting this disease based on machine and deep learning methods is necessary for early detection in order to provide the appropriate treatment as early as possible to patients. Thus, the objective of this work is to determine the most relevant parameters with respect to the different classifiers used for keratoconus classification based on the keratoconus dataset of Harvard Dataverse. A total of 446 parameters are analyzed out of 3162 observations by 11 different feature selection algorithms. Obtained results showed that sequential forward selection (SFS) method provided a subset of 10 most relevant variables, thus, generating the highest classification performance by the application of random forest (RF) classifier, with an accuracy of 98% and 95% considering 2 and 4 keratoconus classes, respectively. Found classification accuracy applying RF classifier on the selected variables using SFS method achieves the accuracy obtained using all features of the original dataset.Mustapha AatilaMohamed LachgarHrimech HamidAli KartitHindawi LimitedarticleComputer applications to medicine. Medical informaticsR858-859.7ENComputational and Mathematical Methods in Medicine, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Mustapha Aatila Mohamed Lachgar Hrimech Hamid Ali Kartit Keratoconus Severity Classification Using Features Selection and Machine Learning Algorithms
description	Keratoconus is a noninflammatory disease characterized by thinning and bulging of the cornea, generally appearing during adolescence and slowly progressing, causing vision impairment. However, the detection of keratoconus remains difficult in the early stages of the disease because the patient does not feel any pain. Therefore, the development of a method for detecting this disease based on machine and deep learning methods is necessary for early detection in order to provide the appropriate treatment as early as possible to patients. Thus, the objective of this work is to determine the most relevant parameters with respect to the different classifiers used for keratoconus classification based on the keratoconus dataset of Harvard Dataverse. A total of 446 parameters are analyzed out of 3162 observations by 11 different feature selection algorithms. Obtained results showed that sequential forward selection (SFS) method provided a subset of 10 most relevant variables, thus, generating the highest classification performance by the application of random forest (RF) classifier, with an accuracy of 98% and 95% considering 2 and 4 keratoconus classes, respectively. Found classification accuracy applying RF classifier on the selected variables using SFS method achieves the accuracy obtained using all features of the original dataset.
format	article
author	Mustapha Aatila Mohamed Lachgar Hrimech Hamid Ali Kartit
author_facet	Mustapha Aatila Mohamed Lachgar Hrimech Hamid Ali Kartit
author_sort	Mustapha Aatila
title	Keratoconus Severity Classification Using Features Selection and Machine Learning Algorithms
title_short	Keratoconus Severity Classification Using Features Selection and Machine Learning Algorithms
title_full	Keratoconus Severity Classification Using Features Selection and Machine Learning Algorithms
title_fullStr	Keratoconus Severity Classification Using Features Selection and Machine Learning Algorithms
title_full_unstemmed	Keratoconus Severity Classification Using Features Selection and Machine Learning Algorithms
title_sort	keratoconus severity classification using features selection and machine learning algorithms
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/100d10b553f941eaabe16c544496b151
work_keys_str_mv	AT mustaphaaatila keratoconusseverityclassificationusingfeaturesselectionandmachinelearningalgorithms AT mohamedlachgar keratoconusseverityclassificationusingfeaturesselectionandmachinelearningalgorithms AT hrimechhamid keratoconusseverityclassificationusingfeaturesselectionandmachinelearningalgorithms AT alikartit keratoconusseverityclassificationusingfeaturesselectionandmachinelearningalgorithms
_version_	1718407753295200256

Keratoconus Severity Classification Using Features Selection and Machine Learning Algorithms

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