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An Optimisation-Driven Prediction Method for Automated Diagnosis and Prognosis

Author

Listed:
  • 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)

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.

Suggested Citation

  • Valentino Santucci & Alfredo Milani & Fabio Caraffini, 2019. "An Optimisation-Driven Prediction Method for Automated Diagnosis and Prognosis," Mathematics, MDPI, vol. 7(11), pages 1-20, November.
    • Handle: RePEc:gam:jmathe:v:7:y:2019:i:11:p:1051-:d:283336
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    References listed on IDEAS

    as
    1. Junhua Hu & Dan Chen & Pei Liang, 2019. "A Novel Interval Three-Way Concept Lattice Model with Its Application in Medical Diagnosis," Mathematics, MDPI, vol. 7(1), pages 1-14, January.
    2. M. Hernaiz-Guijarro & J. C. Castro-Palacio & E. Navarro-Pardo & J. M. Isidro & P. Fernández-de-Córdoba, 2019. "A Probabilistic Classification Procedure Based on Response Time Analysis Towards a Quick Pre-Diagnosis of Student’s Attention Deficit," Mathematics, MDPI, vol. 7(5), pages 1-16, May.
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