Evolutionary strategy to develop learning-based decision systems. Application to breast cancer and liver fibrosis stadialization

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Highlights

  • An evolutionary-based strategy for building decision models is proposed.

  • Five medical datasets (breast cancer and liver fibrosis) were used for assessment.

  • The synergetic decision-making involved is easy to understand and apply.

  • Statistical benchmark showed the effectiveness of the model.

  • The model is expected to easily adapt to different medical decision-making issues.

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.

Keywords

Decision support systems
Evolutionary computing
Machine learning algorithms
Weighted voting system
Breast cancer
Liver fibrosis stadialization

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