A New Hybrid Intelligent System for Prediction of Medical Diseases

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.

[1]  Seong-Whan Lee,et al.  Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis , 2014, NeuroImage.

[2]  Antonia Azzini,et al.  A New Genetic Approach for Neural Network Design , 2008, Engineering Evolutionary Intelligent Systems.

[3]  Xuan Wang,et al.  A Hybrid Approach of Neural Network with Particle Swarm Optimization for Tobacco Pests Prediction , 2008, CCTA.

[4]  F. Wilcoxon.  Individual Comparisons by Ranking Methods , 1945 .

[5]  Siti Mariyam Hj. Shamsuddin,et al.  Enhancement of artificial neural network learning using centripetal accelerated particle swarm optimization for medical diseases diagnosis , 2014, Soft Comput..

[6]  R. Chitra,et al.  Risk Prediction of Heart Disease Based on Swarm Optimized Neural Network , 2014 .

[7]  Salwani Abdullah,et al.  Hybridizing firefly algorithms with a probabilistic neural network for solving classification problems , 2015, Appl. Soft Comput..

[8]  Sung-Bae Cho,et al.  An improved swarm optimized functional link artificial neural network (ISO-FLANN) for classification , 2012, J. Syst. Softw..

[10]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[11]  Sultan Noman Qasem,et al.  Multi-objective hybrid evolutionary algorithms for radial basis function neural network design , 2012, Knowl. Based Syst..

[12]  Hamada R. H. Al-Absi,et al.  Hybrid Intelligent System for Disease Diagnosis Based on Artificial Neural Networks, Fuzzy Logic, and Genetic Algorithms , 2011 .

[13]  Mathew Mithra Noel,et al.  Galactic Swarm Optimization: A new global optimization metaheuristic inspired by galactic motion , 2016, Appl. Soft Comput..

[14]  Sidong Liu,et al.  Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer's Disease , 2015, IEEE Transactions on Biomedical Engineering.

[15]  J. S. Hunter,et al.  Statistics for Experimenters: Design, Innovation, and Discovery , 2006 .

[16]  Chih-Chou Chiu,et al.  Hybrid intelligent modeling schemes for heart disease classification , 2014, Appl. Soft Comput..

[17]  Jacek M. Zurada,et al.  Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance , 2008, Neural Networks.

[18]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[19]  Qeethara Al-Shayea.  Artificial Neural Networks in Medical Diagnosis , 2011 .