Impact of Metaheuristic Iteration on Artificial Neural Network Structure in Medical Data

Abstract

Medical data classification is an important factor in improving diagnosis and treatment and can assist physicians in making decisions about serious diseases by collecting symptoms and medical analyses. In this work, hybrid classification optimization methods such as Genetic Algorithm (GA), Particle Swam Optimization (PSO), and Fireworks Algorithm (FWA), are proposed for enhancing the classification accuracy of the Artificial Neural Network (ANN). The enhancement process is tested through two experiments. First, the proposed algorithms are applied on five benchmark medical data sets from the repository of the University of California in Irvine (UCI). The model with the best results is then used in the second experiment, which focuses on tuning the parameters of the selected algorithm by choosing a different number of iterations in ANNs with different numbers of hidden layers. Enhanced ANN with the three optimization algorithms are tested on biological gene sequence big dataset obtained from The Cancer Genome Atlas (TCGA) repository. GA and FWA are statistically significant but PSO was statistically not, and GA overcame PSO and FWA in performance. The methodology is successful and registers improvements in every step, as significant results are obtained.Processes 2018, 6, 57 2 of 17 neural networks. By contrast, ant colony optimization algorithm versions for continuous parameter optimization have been used to train the weights of neural networks [8]. Mizuta et al. [9] proposed genetic algorithms to design and train neural networks, in order to obtain the best network structure and the optimized parameter set concurrently. Meanwhile, a fitness function depending on output errors and simplicity in the structure of the network is introduced. Blum and Socha initially presented an ant colony optimization approach for discrete optimization problems [10].Örkcü et al. developed a hybrid intelligent model (hybrid genetic algorithm and simulated annealing) for training ANNs that aim to exploit the advantages of the genetic and simulated annealing algorithms and alleviate their limitations. The model was applied to three benchmark data sets, namely, Breast Cancer Wisconsin (WDBC), Pima Indians Diabetes (PID), and Liver Disorders (LD) [5].Seera and Lim proposed a hybrid intelligent system that integrates fuzzy min-max neural network with classification and regression tree and random forest. This hybrid system aims to exploit the advantages and reduce the limitations of the constituent models, learn incrementally using fuzzy min-max neural network, explain its predicted outputs with classification and regression tree, and achieve high classification performance by random forest [11].Dutta et al. proposed an improved firework with an ANN model for classifying five benchmark data sets from the University of California, Irvine (UCI) repository [2,12].In Zainuddin et al. [13], a wavelet neural network was used as a classifier on two epileptic seizure benchmark data sets from the UCI repository; an algorithm based on enhanced harmo...

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