Automatic migraine classification using artificial neural networks [version 2; peer review: 1 approved, 2 approved with reservations]

Background: Previous studies of migraine classification have focused on the analysis of brain waves, leading to the development of complex tests that are not accessible to the majority of the population. In the early stages of this pathology, patients tend to go to the emergency services or outpatie...

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Autores principales:	Paola A. Sanchez-Sanchez, José Rafael García-González, Juan Manuel Rúa Ascar
Formato:	article
Lenguaje:	EN
Publicado:	F1000 Research Ltd 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/63c658ca659c41fba9fe1df68db6c42e
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spelling	oai:doaj.org-article:63c658ca659c41fba9fe1df68db6c42e2021-11-08T10:53:47ZAutomatic migraine classification using artificial neural networks [version 2; peer review: 1 approved, 2 approved with reservations]2046-140210.12688/f1000research.23181.2https://doaj.org/article/63c658ca659c41fba9fe1df68db6c42e2020-07-01T00:00:00Zhttps://f1000research.com/articles/9-618/v2https://doaj.org/toc/2046-1402Background: Previous studies of migraine classification have focused on the analysis of brain waves, leading to the development of complex tests that are not accessible to the majority of the population. In the early stages of this pathology, patients tend to go to the emergency services or outpatient department, where timely identification largely depends on the expertise of the physician and continuous monitoring of the patient. However, owing to the lack of time to make a proper diagnosis or the inexperience of the physician, migraines are often misdiagnosed either because they are wrongly classified or because the disease severity is underestimated or disparaged. Both cases can lead to inappropriate, unnecessary, or imprecise therapies, which can result in damage to patients’ health. Methods: This study focuses on designing and testing an early classification system capable of distinguishing between seven types of migraines based on the patient’s symptoms. The methodology proposed comprises four steps: data collection based on symptoms and diagnosis by the treating physician, selection of the most relevant variables, use of artificial neural network models for automatic classification, and selection of the best model based on the accuracy and precision of the diagnosis. Results: The artificial neural network models used provide an excellent classification performance, with accuracy and precision levels >97% and which exceed the classifications made using other model, such as logistic regression, support vector machines, nearest neighbor, and decision trees. Conclusions: The implementation of migraine classification through artificial neural networks is a powerful tool that reduces the time to obtain accurate, reliable, and timely clinical diagnoses.Paola A. Sanchez-SanchezJosé Rafael García-GonzálezJuan Manuel Rúa AscarF1000 Research LtdarticleMedicineRScienceQENF1000Research, Vol 9 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Paola A. Sanchez-Sanchez José Rafael García-González Juan Manuel Rúa Ascar Automatic migraine classification using artificial neural networks [version 2; peer review: 1 approved, 2 approved with reservations]
description	Background: Previous studies of migraine classification have focused on the analysis of brain waves, leading to the development of complex tests that are not accessible to the majority of the population. In the early stages of this pathology, patients tend to go to the emergency services or outpatient department, where timely identification largely depends on the expertise of the physician and continuous monitoring of the patient. However, owing to the lack of time to make a proper diagnosis or the inexperience of the physician, migraines are often misdiagnosed either because they are wrongly classified or because the disease severity is underestimated or disparaged. Both cases can lead to inappropriate, unnecessary, or imprecise therapies, which can result in damage to patients’ health. Methods: This study focuses on designing and testing an early classification system capable of distinguishing between seven types of migraines based on the patient’s symptoms. The methodology proposed comprises four steps: data collection based on symptoms and diagnosis by the treating physician, selection of the most relevant variables, use of artificial neural network models for automatic classification, and selection of the best model based on the accuracy and precision of the diagnosis. Results: The artificial neural network models used provide an excellent classification performance, with accuracy and precision levels >97% and which exceed the classifications made using other model, such as logistic regression, support vector machines, nearest neighbor, and decision trees. Conclusions: The implementation of migraine classification through artificial neural networks is a powerful tool that reduces the time to obtain accurate, reliable, and timely clinical diagnoses.
format	article
author	Paola A. Sanchez-Sanchez José Rafael García-González Juan Manuel Rúa Ascar
author_facet	Paola A. Sanchez-Sanchez José Rafael García-González Juan Manuel Rúa Ascar
author_sort	Paola A. Sanchez-Sanchez
title	Automatic migraine classification using artificial neural networks [version 2; peer review: 1 approved, 2 approved with reservations]
title_short	Automatic migraine classification using artificial neural networks [version 2; peer review: 1 approved, 2 approved with reservations]
title_full	Automatic migraine classification using artificial neural networks [version 2; peer review: 1 approved, 2 approved with reservations]
title_fullStr	Automatic migraine classification using artificial neural networks [version 2; peer review: 1 approved, 2 approved with reservations]
title_full_unstemmed	Automatic migraine classification using artificial neural networks [version 2; peer review: 1 approved, 2 approved with reservations]
title_sort	automatic migraine classification using artificial neural networks [version 2; peer review: 1 approved, 2 approved with reservations]
publisher	F1000 Research Ltd
publishDate	2020
url	https://doaj.org/article/63c658ca659c41fba9fe1df68db6c42e
work_keys_str_mv	AT paolaasanchezsanchez automaticmigraineclassificationusingartificialneuralnetworksversion2peerreview1approved2approvedwithreservations AT joserafaelgarciagonzalez automaticmigraineclassificationusingartificialneuralnetworksversion2peerreview1approved2approvedwithreservations AT juanmanuelruaascar automaticmigraineclassificationusingartificialneuralnetworksversion2peerreview1approved2approvedwithreservations
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Automatic migraine classification using artificial neural networks [version 2; peer review: 1 approved, 2 approved with reservations]

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