Publication
Accuracy Optimization in Speech Pathology Diagnosis with Data Preprocessing Techniques
| dc.contributor.author | Fernandes, Joana Filipa Teixeira | |
| dc.contributor.author | Freitas, Diamantino Rui | |
| dc.contributor.author | Teixeira, João Paulo | |
| dc.date.accessioned | 2024-10-07T10:59:18Z | |
| dc.date.available | 2024-10-07T10:59:18Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | Using acoustic analysis to classify and identify speech disorders noninvasively can reduce waiting times for patients and specialists while also increasing the accuracy of diagnoses. In order to identify models to use in a vocal disease diagnosis system, we want to know which models have higher success rates in distinguishing between healthy and pathological sounds. For this purpose, 708 diseased people spread throughout 19 pathologies, and 194 control people were used. There are nine sound files per subject, three vowels in three tones, for each subject. From each sound file, 13 parameters were extracted. For the classification of healthy/pathological individuals, a variety of classifiers based on Machine Learning models were used, including decision trees, discriminant analyses, logistic regression classifiers, naive Bayes classifiers, support vector machines, classifiers of closely related variables, ensemble classifiers and artificial neural network classifiers. For each patient, 118 parameters were used initially. The first analysis aimed to find the best classifier, thus obtaining an accuracy of 81.3% for the Ensemble Sub-space Discriminant classifier. The second and third analyses aimed to improve ground accuracy using preprocessingmethodologies. Therefore, in the second analysis, the PCA technique was used, with an accuracy of 80.2%. The third analysis combined several outlier treatment models with several data normalizationmodels and, in general, accuracy improved, obtaining the best accuracy (82.9%) with the combination of the Greebs model for outliers treatment and the range model for the normalization of data procedure. | pt_PT |
| dc.description.sponsorship | The work was supported by the Foundation for Science and Technology UIDB/05757/2020, UIDP/05757/2020 and 2021.04729.BD and by SusTEC LA/P/0007/2021. The authors acknowledge the financial support for FEUP for this publication. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.citation | Fernandes, Joana Filipa Teixeira; Freitas, Diamantino Rui; Teixeira, João Paulo (2024). Accuracy Optimization in Speech Pathology Diagnosis with Data Preprocessing Techniques. In 3rd International Conference on Optimization, Learning Algorithms and Applications (OL2A 2023). Cham: Springer Nature, Vol. 1, p. 287–299. ISBN 978-3-031-53024-1. | pt_PT |
| dc.identifier.doi | 10.1007/978-3-031-53025-8_20 | pt_PT |
| dc.identifier.isbn | 978-3-031-53024-1 | |
| dc.identifier.isbn | 978-3-031-53025-8 | |
| dc.identifier.uri | http://hdl.handle.net/10198/30321 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | Springer Nature | pt_PT |
| dc.relation | Research Centre in Digitalization and Intelligent Robotics | |
| dc.relation | Research Centre in Digitalization and Intelligent Robotics | |
| dc.relation | Associate Laboratory for Sustainability and Tecnology in Mountain Regions | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Outliers | pt_PT |
| dc.subject | Normalization | pt_PT |
| dc.subject | Speech Pathologies | pt_PT |
| dc.subject | Speech Features | pt_PT |
| dc.subject | Machine Learning | pt_PT |
| dc.subject | Vocal Acoustic Analysis | pt_PT |
| dc.title | Accuracy Optimization in Speech Pathology Diagnosis with Data Preprocessing Techniques | pt_PT |
| dc.type | conference paper | |
| dspace.entity.type | Publication | |
| oaire.awardTitle | Research Centre in Digitalization and Intelligent Robotics | |
| oaire.awardTitle | Research Centre in Digitalization and Intelligent Robotics | |
| oaire.awardTitle | Associate Laboratory for Sustainability and Tecnology in Mountain Regions | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F05757%2F2020/PT | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT//2021.04729.BD/PT | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/LA%2FP%2F0007%2F2020/PT | |
| oaire.citation.endPage | 299 | pt_PT |
| oaire.citation.startPage | 287 | pt_PT |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| person.familyName | Fernandes | |
| person.familyName | Teixeira | |
| person.givenName | Joana Filipa Teixeira | |
| person.givenName | João Paulo | |
| person.identifier | ABC-9055-2020 | |
| person.identifier | 663194 | |
| person.identifier.ciencia-id | AE12-440A-299D | |
| person.identifier.ciencia-id | 4F15-B322-59B4 | |
| person.identifier.orcid | 0000-0002-0618-4627 | |
| person.identifier.orcid | 0000-0002-6679-5702 | |
| person.identifier.rid | N-6576-2013 | |
| person.identifier.scopus-author-id | 57069567500 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | conferenceObject | pt_PT |
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