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Context-based probability neural network classifiers realized by genetic optimization for medical decision making

机译：通过遗传优化实现的基于上下文的概率神经网络分类器用于医疗决策

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摘要

In this paper, we proposed context-based probability neural network (CPNN) classifiers for solving a medical decision making problems. The concept of “contexts” coming from the clustering research area is explored here to construct the second layer of probability neural network classifiers. Furthermore, genetic algorithm is used to optimize the structure parameters when designing the proposed CPNN. In contrast to the known probability neural networks, the proposed CPNN archive a better accuracy classification rate. Several known data sets are utilized to evaluate the performance of CPNN. Experimental results demonstrate that the relationship between the selected features and disease are more apparent in comparison with the conventional neural network models.
机译：在本文中，我们提出了基于上下文的概率神经网络（CPNN）分类器来解决医疗决策问题。这里探讨了来自聚类研究领域的“上下文”概念，以构建第二层概率神经网络分类器。此外，在设计所提出的CPNN时，使用遗传算法来优化结构参数。与已知的概率神经网络相反，提出的CPNN归档了更好的准确性分类率。利用几个已知的数据集来评估CPNN的性能。实验结果表明，与传统的神经网络模型相比，所选特征与疾病之间的关系更加明显。

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