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Open AccessArticle

An Optimisation-Driven Prediction Method for Automated Diagnosis and Prognosis

1
Department of Humanities and Social Sciences, University for Foreigners of Perugia, piazza G. Spitella 3, 06123 Perugia, Italy
2
Department of Mathematics and Computer Science, University of Perugia, via Vanvitelli 1, 06123 Perugia, Italy
3
Institute of Artificial Intelligence, School of Computer Science and Informatics, De Montfort University, The Gateway, Leicester LE1 9BH, UK
*
Author to whom correspondence should be addressed.
Mathematics 2019, 7(11), 1051; https://doi.org/10.3390/math7111051
Received: 7 September 2019 / Revised: 15 October 2019 / Accepted: 23 October 2019 / Published: 4 November 2019
(This article belongs to the Special Issue Numerical Optimization and Applications)
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. View Full-Text
Keywords: automated diagnosis; particle swarm optimization; estimation of distribution algorithms; classification; hybrid algorithms automated diagnosis; particle swarm optimization; estimation of distribution algorithms; classification; hybrid algorithms
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Santucci, V.; Milani, A.; Caraffini, F. An Optimisation-Driven Prediction Method for Automated Diagnosis and Prognosis. Mathematics 2019, 7, 1051.

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