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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

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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...

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

URI: https://cronfa.swan.ac.uk/Record/cronfa60940
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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: College of Science
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