pubs2024

@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}

}

@inproceedings{Peixoto2018285,

title = {Iron value classification in patients undergoing continuous ambulatory peritoneal dialysis using data mining},

author = {C. Peixoto and H. Peixoto and J. Machado and A. Abelha and M. F. Santos},

editor = {Ziefle M. Bamidis P.D. Bamidis P.D.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85052307397&doi=10.5220%2f0006820802850290&partnerID=40&md5=16d6619af21cc9be3469325ea0022e96},

doi = {10.5220/0006820802850290},

isbn = {9789897582998},

year  = {2018},

date = {2018-01-01},

journal = {ICT4AWE 2018 - Proceedings of the 4th International Conference on Information and Communication Technologies for Ageing Well and e-Health},

volume = {2018-March},

pages = {285-290},

publisher = {SciTePress},

abstract = {In this article, Data Mining classification techniques are employed, in order to classify as normal or not-normal the iron values from a patients’ blood analysis. The dataset used is relative to patients that were subjected to Continuous Ambulatory Peritoneal Dialysis (CAPD) treatment. Weka software was used for testing several classification algorithms into such data set. The main purpose is finding the best suitable classification algorithm, with a pleasing performance in classifying the instances of the data, whereas preserving low rate of false positives. The IBk algorithm achieved the best performance, being able to correctly classify 97.39% of the instances. Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.},

note = {cited By 1; Conference of 4th International Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE 2018 ; Conference Date: 22 March 2018 Through 23 March 2018; Conference Code:135921},

keywords = {Blood analysis; Classification algorithm; Data set; False positive; Low rates; Mining classification; Peritoneal dialysis; Weka, Classification (of information), Data mining; Dialysis; Iron; Patient treatment; Software testing; Statistical tests},

pubstate = {published},

tppubtype = {inproceedings}

}