Enhancement of artificial neural network learning using centripetal accelerated particle swarm optimization for medical diseases diagnosis

Beheshti, Zahra; Shamsuddin, Siti Mariyam; Beheshti, Ebrahim; Yuhaniz, Siti Sophiayati

doi:10.1007/s00500-013-1198-0

Cited by 68 publications

(47 citation statements)

References 64 publications

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“…Previously many authors tried to create systems of Computational Intelligence for medical diagnostics tasks using different datasets from Medical Repositories as an input data [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38]. But all these systems have common disadvantages, like impossibility to process data in online mode, fixed number of features and diagnoses, low rate of convergence.…”

Section: Introductionmentioning

confidence: 99%

Deep Hybrid System of Computational Intelligence with Architecture Adaptation for Medical Fuzzy Diagnostics

Perova¹,

Pliss²

2017

IJISA

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Abstract-In the paper the deep hybrid system of computational intelligence with architecture adaptation for medical fuzzy diagnostics is proposed. This system allows to increase a quality of medical information processing under the condition of overlapping classes due to special adaptive architecture and training algorithms. The deep hybrid system under consideration can tune its architecture in situation when number of features and diagnoses can be variable. The special algorithms for its training are developed and optimized for situation of different system architectures without retraining of synaptic weights that have been tuned at previous steps. The proposed system was used for processing of three medical data sets (dermatology dataset, Pima Indians diabetes dataset and Parkinson disease dataset) under the condition of fixed number of features and diagnoses and in situation of its increasing. A number of conducted experiments have shown high quality of medical diagnostic process and confirmed the efficiency of the deep hybrid system of computational intelligence with architecture adaptation for medical fuzzy diagnostics.

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Section: Introductionmentioning

confidence: 99%

Deep Hybrid System of Computational Intelligence with Architecture Adaptation for Medical Fuzzy Diagnostics

Perova¹,

Pliss²

2017

IJISA

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“…The proposed system is able to reduce medical mistakes and provide high accuracy prediction. Beheshti et al [10] proposed a new meta-heuristics approach named Centripetal Accelerated Particle Swarm Optimization (CAPSO). The proposed approach is used to enhance the performance of ANN learning and accuracy.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid ANN-GWO Algorithm for Prediction of Heart Disease

Turabieh¹

2016

AJOR

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The paper investigates the powerful of hybridizing two computational intelligence methods viz., Gray Wolf Optimization (GWO) and Artificial Neural Networks (ANN) for prediction of heart disease. Gray wolf optimization is a global search method while gradient-based back propagation method is a local search one. The proposed algorithm implies the ability of ANN to find a relationship between the input and the output variables while the stochastic search ability of GWO is used for finding the initial optimal weights and biases of the ANN to reduce the probability of ANN getting stuck at local minima and slowly converging to global optimum. For evaluation purpose, the performance of hybrid model (ANN-GWO) was compared with standard back-propagation neural network (BPNN) using Root Mean Square Error (RMSE). The results demonstrate that the proposed model increases the convergence speed and the accuracy of prediction.

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“…10 shows a summary of the comparative results. Note that none of the algorithms that presented in Table XI (MEPGANf1-f3 [12], MLP-BP [13], ISO-FLANN [13], NN-CAPSO [14], NN-GSA [14], NN-ICA [14], NN-BP [15], NN-MVO [15], MODE-ESNN [16], DPM [16], SAE-MR Over all, NN-GSO algorithm is better or at least competitive for breast cancer, diabetes, heart, hepatitis, appendicitis and Alzheimer. On the other hand, NN-GSO performs comparable for the liver, disorders, thyroid and dermatology datasets (with respect to classification accuracy) comparing to other algorithms.…”

Section: B Mean Squared Error Analysismentioning

confidence: 99%

“…after crossover, the mutation process is applied to individuals by modifying some genes in the strings. Until a satisfactory solution is found, this evaluation selectionreproduction cycle is repeated [7]. The outline of a GA is shown in Fig.…”

Section: B Genetic Algorithmmentioning

confidence: 99%

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A New Hybrid Intelligent System for Prediction of Medical Diseases

Qasem¹,

Alsaidan²

2018

ijacsa

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Abstract-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.

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Enhancement of artificial neural network learning using centripetal accelerated particle swarm optimization for medical diseases diagnosis

Cited by 68 publications

References 64 publications

Deep Hybrid System of Computational Intelligence with Architecture Adaptation for Medical Fuzzy Diagnostics

Deep Hybrid System of Computational Intelligence with Architecture Adaptation for Medical Fuzzy Diagnostics

A Hybrid ANN-GWO Algorithm for Prediction of Heart Disease

A New Hybrid Intelligent System for Prediction of Medical Diseases

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