A new hybrid metaheuristic for medical data classification. (1st January 2014)

• Record Type: Journal Article
• Authors: AlMuhaideb, Sarab; Menai, Mohamed El Bachir
• Is Part Of: International journal of metaheuristics. Volume 3:Number 1(2014)
• Page Start: 59
• Page End: 80
• Publication Date: 2014-01-01
• Subjects: medical decision support -- classification -- ant colony optimisation -- ACO -- genetic algorithms -- sequential covering -- hybrid metaheuristics; Heuristic algorithms -- Periodicals; 006.3105
• Languages: English
• ISSNs: 1755-2176

Abstract

The classification of medical data is a complex task. Medical diagnosis and/or prognosis can be modelled as classification tasks. A hybrid metaheuristic is introduced consisting of two phases; an ant colony optimisation (ACO) phase and a genetic algorithm (GA) phase. The population of the GA is initialised to decision lists constructed during the ACO phase using different subsets of the training data. The task of the GA is to optimise the decision lists obtained in terms of classification accuracy and model size. Results on a number of benchmark real-world medical datasets show the usefulness of the proposed approach.