Journal article 592 views 72 downloads
An Optimisation-Driven Prediction Method for Automated Diagnosis and Prognosis
,
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)
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...
| 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!
|
| 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. |
|---|---|
| Keywords: |
automated diagnosis; particle swarm optimization; estimation of distribution algorithms; classification; hybrid algorithms |
| College: |
Faculty of Science and Engineering |
| 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”. |
| Issue: |
11 |
| Start Page: |
1051 |