期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2018
卷号：9
期号：4
DOI：10.14569/IJACSA.2018.090452
出版社：Science and Information Society (SAI)
摘要：This paper proposes a hybrid intelligent system as medical decision support tool for data classification based on the Neural Network, Galactic Swarm Optimization (NN-GSO), and the classification model. The goal of the hybrid intelligent system is to take the advantages and reduce the disadvantages of the constituent models. The system is capable of learning from data sets and reach great classification performance. Consequently, various algorithms have been developed that include Neural Network based on Galactic Swarm Optimization (NN-GSO), Neural Network based on Particle Swarm Optimization (NN-PSO) and Neural Network based on Genetic Algorithm (NN-GA) to improve NN structure and accuracy rates. For the evaluation process, the hybrid intelligent system has used multiple of benchmark medical data sets to evaluate the effectiveness. These benchmarks were gotten from the UCI Repository of Machine Learning. The three-performance metrics were calculated are accuracy, sensitivity and specificity. These metrics are useful for medical applications. The proposed algorithm was tested on various data sets which represent binary and multi-class medical diseases problems. The proposed algorithm performance analyzed and compared with others using k-fold cross validation. The significance tests results have proven that the proposed algorithm is effective to solve neural networks with good generalization ability and network structure for medical diseases detection.
关键词：Artificial neural network; galactic swarm optimization; particle swarm optimization; genetic algorithm; hybrid intelligent system; medical decision support
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