2022
Neto, C.; Ferreira, D.; Nunes, J.; Braga, L.; Martins, L.; Cunha, L.; Machado, J.
Classification of Dementia in Adults Proceedings Article
Em: A., Riola Rodriguez J. M. Fajardo-Toro C. H. Rocha (Ed.): pp. 283-293, Springer Science and Business Media Deutschland GmbH, 2022, ISSN: 21903018, (cited By 0; Conference of Multidisciplinary International Conference of Research Applied to Defense and Security, MICRADS 2021 ; Conference Date: 18 August 2021 Through 20 August 2021; Conference Code:267889).
Resumo | Links | BibTeX | Etiquetas: Alzheimer; Clinical conditions; Condition; Cross industry; Data mining process; Industry standards; Machine decisions; Machine learning algorithms; Standards process; Support vectors machine, Classification (of information), Data mining; Decision trees; Diagnosis; Learning algorithms; Magnetic resonance imaging; Neurodegenerative diseases; Support vector machines
@inproceedings{Neto2022283,
title = {Classification of Dementia in Adults},
author = {C. Neto and D. Ferreira and J. Nunes and L. Braga and L. Martins and L. Cunha and J. Machado},
editor = {Riola Rodriguez J. M. Fajardo-Toro C.H. Rocha A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119352719&doi=10.1007%2f978-981-16-4884-7_23&partnerID=40&md5=c3b7bd8b729c23c8a694e7e6807db3cd},
doi = {10.1007/978-981-16-4884-7_23},
issn = {21903018},
year = {2022},
date = {2022-01-01},
journal = {Smart Innovation, Systems and Technologies},
volume = {255},
pages = {283-293},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Dementia is a broad term for a large number of conditions, and it is often associated with Alzheimer’s disease. A reliable diagnosis of this disease, especially in the early stages, may prevent further complications. As such, machine learning algorithms can be applied in order to validate and correctly classify cases of dementia or non dementia in adults, assisting physicians in the diagnosis and management of this clinical condition. In this study, a dataset containing magnetic resonance imaging comparisons of demented/non demented adults was used to conduct a Data Mining process, following the Cross Industry Standard Process for Data Mining methodology, with the main goal of classifying instances of dementia. Different machine learning algorithms were applied during this process, more specifically Support Vector Machines, Decision Trees, Logistic Regression, Neural Networks, Naïve Bayes and Random Forest. The maximum accuracy of 95.41% was achieved with the Naïve Bayes algorithm using Split Validation. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.},
note = {cited By 0; Conference of Multidisciplinary International Conference of Research Applied to Defense and Security, MICRADS 2021 ; Conference Date: 18 August 2021 Through 20 August 2021; Conference Code:267889},
keywords = {Alzheimer; Clinical conditions; Condition; Cross industry; Data mining process; Industry standards; Machine decisions; Machine learning algorithms; Standards process; Support vectors machine, Classification (of information), Data mining; Decision trees; Diagnosis; Learning algorithms; Magnetic resonance imaging; Neurodegenerative diseases; Support vector machines},
pubstate = {published},
tppubtype = {inproceedings}
}