An Optimisation-Driven Prediction Method for Automated Diagnosis and Prognosis

Valentino Santucci, Alfredo Milani and Fabio Caraffini
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Valentino Santucci: Department of Humanities and Social Sciences, University for Foreigners of Perugia, piazza G. Spitella 3, 06123 Perugia, Italy
Alfredo Milani: Department of Mathematics and Computer Science, University of Perugia, via Vanvitelli 1, 06123 Perugia, Italy
Fabio Caraffini: Institute of Artificial Intelligence, School of Computer Science and Informatics, De Montfort University, The Gateway, Leicester LE1 9BH, UK

Mathematics, 2019, vol. 7, issue 11, 1-20

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 (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2019
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