No Cover Image

Journal article 472 views 50 downloads

An Optimisation-Driven Prediction Method for Automated Diagnosis and Prognosis

Valentino Santucci Orcid Logo, Alfredo Milani Orcid Logo, Fabio Caraffini Orcid Logo

Mathematics, Volume: 7, Issue: 11, Start page: 1051

Swansea University Author: Fabio Caraffini Orcid Logo

  • 60940_VoR.pdf

    PDF | Version of Record

    Copyright: 2019 by the authors. This is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license

    Download (340.57KB)

Check full text

DOI (Published version): 10.3390/math7111051

Abstract

This article presents a novel hybrid classification paradigm for medical diagnoses and prognoses prediction. The core mechanism of the proposed method relies on a centroid classification algorithm whose logic is exploited to formulate the classification task as a real-valued optimisation problem. A...

Full description

Published in: Mathematics
ISSN: 2227-7390
Published: MDPI AG 2019
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa60940
Tags: Add Tag
No Tags, Be the first to tag this record!
first_indexed 2022-09-21T14:06:55Z
last_indexed 2023-01-13T19:21:26Z
id cronfa60940
recordtype SURis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2022-09-21T15:08:16.3840034</datestamp><bib-version>v2</bib-version><id>60940</id><entry>2022-08-28</entry><title>An Optimisation-Driven Prediction Method for Automated Diagnosis and Prognosis</title><swanseaauthors><author><sid>d0b8d4e63d512d4d67a02a23dd20dfdb</sid><ORCID>0000-0001-9199-7368</ORCID><firstname>Fabio</firstname><surname>Caraffini</surname><name>Fabio Caraffini</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2022-08-28</date><deptcode>SCS</deptcode><abstract>This article presents a novel hybrid classification paradigm for medical diagnoses and prognoses prediction. The core mechanism of the proposed method relies on a centroid classification algorithm whose logic is exploited to formulate the classification task as a real-valued optimisation problem. A novel metaheuristic combining the algorithmic structure of Swarm Intelligence optimisers with the probabilistic search models of Estimation of Distribution Algorithms is designed to optimise such a problem, thus leading to high-accuracy predictions. This method is tested over 11 medical datasets and compared against 14 cherry-picked classification algorithms. Results show that the proposed approach is competitive and superior to the state-of-the-art on several occasions.</abstract><type>Journal Article</type><journal>Mathematics</journal><volume>7</volume><journalNumber>11</journalNumber><paginationStart>1051</paginationStart><paginationEnd/><publisher>MDPI AG</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic>2227-7390</issnElectronic><keywords>automated diagnosis; particle swarm optimization; estimation of distribution algorithms; classification; hybrid algorithms</keywords><publishedDay>4</publishedDay><publishedMonth>11</publishedMonth><publishedYear>2019</publishedYear><publishedDate>2019-11-04</publishedDate><doi>10.3390/math7111051</doi><url/><notes/><college>COLLEGE NANME</college><department>Computer Science</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>SCS</DepartmentCode><institution>Swansea University</institution><apcterm/><funders>The research described in this work has been partially supported by: the research grant &#x201C;Fondi per i progetti di ricerca scientifica di Ateneo 2019&#x201D; of the University for Foreigners of Perugia under the project &#x201C;Algoritmi evolutivi per problemi di ottimizzazione e modelli di apprendimento automatico con applicazioni al Natural Language Processing&#x201D;.</funders><projectreference/><lastEdited>2022-09-21T15:08:16.3840034</lastEdited><Created>2022-08-28T20:14:22.5985582</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Mathematics and Computer Science - Computer Science</level></path><authors><author><firstname>Valentino</firstname><surname>Santucci</surname><orcid>0000-0003-1483-7998</orcid><order>1</order></author><author><firstname>Alfredo</firstname><surname>Milani</surname><orcid>0000-0003-4534-1805</orcid><order>2</order></author><author><firstname>Fabio</firstname><surname>Caraffini</surname><orcid>0000-0001-9199-7368</orcid><order>3</order></author></authors><documents><document><filename>60940__25188__c3b3658f8f41498288dda0e677af5e0a.pdf</filename><originalFilename>60940_VoR.pdf</originalFilename><uploaded>2022-09-21T15:07:15.1286562</uploaded><type>Output</type><contentLength>348742</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>Copyright: 2019 by the authors. This is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>http://creativecommons.org/licenses/by/4.0/</licence></document></documents><OutputDurs/></rfc1807>
spelling 2022-09-21T15:08:16.3840034 v2 60940 2022-08-28 An Optimisation-Driven Prediction Method for Automated Diagnosis and Prognosis d0b8d4e63d512d4d67a02a23dd20dfdb 0000-0001-9199-7368 Fabio Caraffini Fabio Caraffini true false 2022-08-28 SCS This article presents a novel hybrid classification paradigm for medical diagnoses and prognoses prediction. The core mechanism of the proposed method relies on a centroid classification algorithm whose logic is exploited to formulate the classification task as a real-valued optimisation problem. A novel metaheuristic combining the algorithmic structure of Swarm Intelligence optimisers with the probabilistic search models of Estimation of Distribution Algorithms is designed to optimise such a problem, thus leading to high-accuracy predictions. This method is tested over 11 medical datasets and compared against 14 cherry-picked classification algorithms. Results show that the proposed approach is competitive and superior to the state-of-the-art on several occasions. Journal Article Mathematics 7 11 1051 MDPI AG 2227-7390 automated diagnosis; particle swarm optimization; estimation of distribution algorithms; classification; hybrid algorithms 4 11 2019 2019-11-04 10.3390/math7111051 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University The research described in this work has been partially supported by: the research grant “Fondi per i progetti di ricerca scientifica di Ateneo 2019” of the University for Foreigners of Perugia under the project “Algoritmi evolutivi per problemi di ottimizzazione e modelli di apprendimento automatico con applicazioni al Natural Language Processing”. 2022-09-21T15:08:16.3840034 2022-08-28T20:14:22.5985582 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Valentino Santucci 0000-0003-1483-7998 1 Alfredo Milani 0000-0003-4534-1805 2 Fabio Caraffini 0000-0001-9199-7368 3 60940__25188__c3b3658f8f41498288dda0e677af5e0a.pdf 60940_VoR.pdf 2022-09-21T15:07:15.1286562 Output 348742 application/pdf Version of Record true Copyright: 2019 by the authors. This is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license true eng http://creativecommons.org/licenses/by/4.0/
title An Optimisation-Driven Prediction Method for Automated Diagnosis and Prognosis
spellingShingle An Optimisation-Driven Prediction Method for Automated Diagnosis and Prognosis
Fabio Caraffini
title_short An Optimisation-Driven Prediction Method for Automated Diagnosis and Prognosis
title_full An Optimisation-Driven Prediction Method for Automated Diagnosis and Prognosis
title_fullStr An Optimisation-Driven Prediction Method for Automated Diagnosis and Prognosis
title_full_unstemmed An Optimisation-Driven Prediction Method for Automated Diagnosis and Prognosis
title_sort An Optimisation-Driven Prediction Method for Automated Diagnosis and Prognosis
author_id_str_mv d0b8d4e63d512d4d67a02a23dd20dfdb
author_id_fullname_str_mv d0b8d4e63d512d4d67a02a23dd20dfdb_***_Fabio Caraffini
author Fabio Caraffini
author2 Valentino Santucci
Alfredo Milani
Fabio Caraffini
format Journal article
container_title Mathematics
container_volume 7
container_issue 11
container_start_page 1051
publishDate 2019
institution Swansea University
issn 2227-7390
doi_str_mv 10.3390/math7111051
publisher MDPI AG
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
active_str 0
description This article presents a novel hybrid classification paradigm for medical diagnoses and prognoses prediction. The core mechanism of the proposed method relies on a centroid classification algorithm whose logic is exploited to formulate the classification task as a real-valued optimisation problem. A novel metaheuristic combining the algorithmic structure of Swarm Intelligence optimisers with the probabilistic search models of Estimation of Distribution Algorithms is designed to optimise such a problem, thus leading to high-accuracy predictions. This method is tested over 11 medical datasets and compared against 14 cherry-picked classification algorithms. Results show that the proposed approach is competitive and superior to the state-of-the-art on several occasions.
published_date 2019-11-04T04:19:28Z
_version_ 1763754291441434624
score 11.017797

Similar Items