title: Evolutionary strategy to develop learning-based decision systems. Application to breast cancer and liver fibrosis stadialization.
abstract: The purpose of this paper is twofold: first, to propose an evolutionary-based method for building a decision model and, second, to assess and validate the model's performance using five different real-world medical datasets (breast cancer and liver fibrosis) by comparing it with state-of-the-art machine learning techniques. The evolutionary-inspired approach has been used to develop the learning-based decision model in the following manner: the hybridization of algorithms has been considered as "crossover", while the development of new variants which can be thought of as "mutation". An appropriate hierarchy of the component algorithms was established based on a statistically built fitness measure. A synergetic decision-making process, based on a weighted voting system, involved the collaboration between the selected algorithms in making the final decision. Well-established statistical performance measures and comparison tests have been extensively used to design and implement the model. Finally, the proposed method has been tested on five medical datasets, out of which four publicly available, and contrasted with state-of-the-art techniques, showing its efficiency in supporting the medical decision-making process.
authors: Department of Biostatistics and Informatics, University of Medicine and Pharmacy of Craiova, Craiova 200349, Romania. Electronic address: gorunef@gmail.com.
authors: Florin
authors: F
authors: Gorunescu
authors: Department of Computer Science, University of Craiova, Craiova 200585, Romania. Electronic address: smaranda.belciug@inf.ucv.ro.
authors: Smaranda
authors: S
authors: Belciug
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000473:pathology*; D020000:Decision Support Systems, Clinical; D005260:Female; D006801:Humans; D007858:Learning; D008103:Liver Cirrhosis / Q000473:pathology*
keywords: Breast cancer; Decision support systems; Evolutionary computing; Liver fibrosis stadialization; Machine learning algorithms; Weighted voting system