An Optimisation-Driven Prediction Method for Automated Diagnosis and Prognosis

Santucci, Valentino; Milani, Alfredo; Caraffini, Fabio

doi:10.3390/math7111051

Journal article 597 views 73 downloads

An Optimisation-Driven Prediction Method for Automated Diagnosis and Prognosis

Valentino Santucci

, Alfredo Milani

, Fabio Caraffini

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

Swansea University Author: Fabio Caraffini

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

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>MACS</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>Mathematics and Computer Science School</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>MACS</DepartmentCode><institution>Swansea University</institution><apcterm/><funders>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”.</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 MACS 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 Mathematics and Computer Science School COLLEGE CODE MACS 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	2019-11-04T02:33:48Z
_version_	1822005268554186752
score	11.048042

An Optimisation-Driven Prediction Method for Automated Diagnosis and Prognosis

Similar Items