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首页> 外文期刊>University of Bucharest. Annals. Mathematical Series >Evolutionary-based intelligent decision model to optimize the liver fibrosis stadialization
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Evolutionary-based intelligent decision model to optimize the liver fibrosis stadialization

机译：基于进化的智能决策模型可优化肝纤维化的稳定性

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ABSTRACT. This paper presents a novel approach to build an intelligent decision system (IDS) inspired by the evolutionary paradigm in order to solve the automatic liver fibrosis stadialization by optimizing the decision-making process.? The evolutionary paradigm was used to answer the basic question: how to distinguish between machine learning algorithms facing a medical decision issue, and how to integrate the most effective of them into IDS, able to provide an optimum decision? In the proposed IDS, a set of well-known neural networks are regarded as the initial population of solutions, and an appropriate hierarchy of algorithms is established fitness-proportionally based on a statistically built fitness measure. Then, the IDS framework is built using the best algorithms and the paradigm of a weighted voting system. In a concrete application, the degrees of liver fibrosis, ranging from F0 (no fibrosis) to F4 (cirrhosis), have been automatically identified in 722 patients with chronic hepatitis C infection using 25 main medical attributes. The decision performance proved significantly superior to the classical approach using standalone algorithms. This approach showed a way to directly and easy optimize the medical decision-making by using the evolutionary paradigm.
机译：抽象。本文提出了一种新颖的方法来构建受进化范式启发的智能决策系统（IDS），以通过优化决策过程来解决肝纤维化的自动稳态化。进化范式用于回答以下基本问题：如何区分面对医学决策问题的机器学习算法，以及如何将最有效的算法集成到IDS中，从而提供最佳决策？在提出的IDS中，将一组众所周知的神经网络视为解决方案的初始种群，并基于统计建立的适应性度量按适应性比例建立适当的算法层次结构。然后，使用最佳算法和加权投票系统的范式构建IDS框架。在一个具体的应用中，使用25种主要医学属性自动识别了722名慢性丙型肝炎患者的肝纤维化程度，从F0（无纤维化）到F4（肝硬化）。事实证明，决策性能明显优于使用独立算法的经典方法。这种方法展示了一种使用进化范式直接轻松地优化医疗决策的方法。

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