Hybrid intelligent modeling schemes for heart disease classification

Shao, Yuehjen E.; Hou, Chia-Ding; Chiu, Chien-Chao

doi:10.1016/j.asoc.2013.09.020

Cited by 101 publications

(51 citation statements)

References 49 publications

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“…NN can be referred to as one of the most widely used classifiers for practical applications [32]. Because the backpropagation neural network (BPNN) is widely used in many applications, this study employs a BPNN when Complexity 5 designing the ANN model.…”

Section: Nn Classifiermentioning

confidence: 99%

Applying Two-Stage Neural Network Based Classifiers to the Identification of Mixture Control Chart Patterns for an SPC-EPC Process

Shao

Chang

2017

Complexity

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The effective controlling and monitoring of an industrial process through the integration of statistical process control (SPC) and engineering process control (EPC) has been widely addressed in recent years. However, because the mixture types of disturbances are often embedded in underlying processes, mixture control chart patterns (MCCPs) are very difficult for an SPC-EPC process to identify. This can result in problems when attempting to determine the underlying root causes of process faults. Additionally, a large number of categories of disturbances may be present in a process, but typical single-stage classifiers have difficulty in identifying large numbers of categories of disturbances in an SPC-EPC process. Therefore, we propose a two-stage neural network (NN) based scheme to enhance the accurate identification rate (AIR) for MCCPs by performing dimension reduction on disturbance categories. The two-stage scheme includes a combination of a NN, support vector machine (SVM), and multivariate adaptive regression splines (MARS). Experimental results reveal that the proposed scheme achieves a satisfactory AIR for identifying MCCPs in an SPC-EPC system.

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Section: Nn Classifiermentioning

confidence: 99%

Applying Two-Stage Neural Network Based Classifiers to the Identification of Mixture Control Chart Patterns for an SPC-EPC Process

Shao

Chang

2017

Complexity

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“…The X 1 , X 2 and X 3 are the best 3 solutions in the population at iteration t. The values of A 1 , A 2 and A 3 are evaluated in Equation (3). The values of α D , β D and δ D are evaluated as shown in Equations (10) (11) and (12) respectively.…”

Section: Gray Wolf Optimizermentioning

confidence: 99%

“…The accuracy of the proposed model is 75% with higher stability. Yuehjen et al [3] proposed a several hybrid models to predict heart disease such as logistic regression (LR), multivariate adaptive regression splines (MARS), artificial neural network (ANN) and rough set (RS). The performance of the proposed models is better than artificial neural network.…”

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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“…When a dataset is large, It is so difficult to train ANN. Yuehjen E. Shao et al [17] proposed a classification method called Hybrid intelligent modeling that hybridized logistic regression (LR), multivariate adaptive regression splines (MARS), artificial neural network (ANN) and rough set (RS) techniques. At initial stage LR, MARS, and RS technique is used to reduce the set of explanatory variable.…”

Section: A Ann For Classificationmentioning

confidence: 99%

A Survey: Hybrid Intelligent Modeling Technique for Data Classification

Rani¹,

Kumar²

2017

International Journal of Advanced Research in Computer and Comm

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Classification is one of the important areas of research in the field of data mining and machine learning. This paper discusses about GA, ANN and SVM algorithm and their use in classification. The artificial neural network is the widely used technique for classification and prediction. ANN has some disadvantage such long learning rate, high computational cost, convergence at local optima and adjustment of weight. Optimization techniques and hybridization improve ANN performance. GA is an optimization technique that produces optimization of the problem by using natural evolution. SVM use the nonlinear kernel functions that implicitly map input data into high-dimensional feature spaces. Hybridization is a technique which combines the two or more classifier to improve the performance of the classifier.

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Hybrid intelligent modeling schemes for heart disease classification

Cited by 101 publications

References 49 publications

Applying Two-Stage Neural Network Based Classifiers to the Identification of Mixture Control Chart Patterns for an SPC-EPC Process

Applying Two-Stage Neural Network Based Classifiers to the Identification of Mixture Control Chart Patterns for an SPC-EPC Process

A Hybrid ANN-GWO Algorithm for Prediction of Heart Disease

A Survey: Hybrid Intelligent Modeling Technique for Data Classification

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