An ANFIS-based model for predicting adequacy of vancomycin regimen using improved genetic algorithm

Ho, Wen-Hsien; Chen, Jianxun; Lee, I-Nong; Su, Hui‐Chen

doi:10.1016/j.eswa.2011.04.109

Cited by 27 publications

(12 citation statements)

References 20 publications

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“…Like a network of brain neurons, an ANN containing multiple layers of simple computing nodes can accurately approximate continuous nonlinear functions and can reveal previously unknown relationships between given input and output variables [8–10]. The unique structure of ANNs is well suited for machine learning methods such as backpropagation [11] and evolutionary algorithms [8, 12, 13]. Because of their universal approximation capability, potential applications of ANNs have attracted interest in some fields [14–18].…”

Section: Introductionmentioning

confidence: 99%

Mortality Predicted Accuracy for Hepatocellular Carcinoma Patients with Hepatic Resection Using Artificial Neural Network

Chiu

Lee

et al. 2013

The Scientific World Journal

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The aim of this present study is firstly to compare significant predictors of mortality for hepatocellular carcinoma (HCC) patients undergoing resection between artificial neural network (ANN) and logistic regression (LR) models and secondly to evaluate the predictive accuracy of ANN and LR in different survival year estimation models. We constructed a prognostic model for 434 patients with 21 potential input variables by Cox regression model. Model performance was measured by numbers of significant predictors and predictive accuracy. The results indicated that ANN had double to triple numbers of significant predictors at 1-, 3-, and 5-year survival models as compared with LR models. Scores of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) of 1-, 3-, and 5-year survival estimation models using ANN were superior to those of LR in all the training sets and most of the validation sets. The study demonstrated that ANN not only had a great number of predictors of mortality variables but also provided accurate prediction, as compared with conventional methods. It is suggested that physicians consider using data mining methods as supplemental tools for clinical decision-making and prognostic evaluation.

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Section: Introductionmentioning

confidence: 99%

Mortality Predicted Accuracy for Hepatocellular Carcinoma Patients with Hepatic Resection Using Artificial Neural Network

Chiu

Lee

et al. 2013

The Scientific World Journal

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“…Also, it was found that the modeling accuracy based on the proposed approach is significantly better than that based on the fuzzy rules, ANFIS, and GA-based ANFIS approaches in terms of mean absolute errors, variance of errors, and the testing error. Ho et al 19 proposed a hybrid Taguchi-genetic algorithm (HTGA) to simultaneously find the optimal premise and consequent parameters and a total output layer parameter of ANFIS by cdirectly maximizing the training accuracy performance criterion. Experimental results show that the HTGA-based ANFIS model outperforms the logistic regression model in terms of prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

A novel hybrid ICA-ANFIS model for prediction of manufacturing processes performance

Baseri

Belali-Owsia

2016

Proceedings of the Institution of Mechanical Engineers, Part E:

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In order to reduce the manufacturing process variability and improve the production yield, it is important to predict the performance of manufacturing process. The adaptive neuro-fuzzy inference system (ANFIS) is a powerful network which can predict the output parameters of manufacturing process. However, design of ANFIS needs trial and errors to select the best structure. In this study, an imperialistic competitive algorithm has been used to determine the ANFIS architecture to reach minimum values of the prediction error. In order to evaluate the performance of this combined method, two illustrative examples of manufacturing processes have been used. Results indicated that the combined method has superiority in prediction of output, rather than previous developed ANFIS models and so it can be applied for modeling of the other manufacturing processes.

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“…The MFNNs have proven particularly effective for nonlinear mapping based on human knowledge and are now attracting interest for use in solving complex classification problems [24]. An MFNN containing layers of simple computing nodes, which is analogous to brain neural networks, has proven effective for approximating nonlinear continuous functions and for revealing previously unknown relationships between given input and output variables [25, 26]. The unique structure of MFNNs enables them to learn by using algorithms such as backpropagation and evolutionary algorithms [31, 32].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Classification Algorithms with Wrapper-Based Feature Selection for Predicting Osteoporosis Outcome Based on Genetic Factors in a Taiwanese Women Population

Chang

Chiu

Kao

et al. 2013

International Journal of Endocrinology

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An essential task in a genomic analysis of a human disease is limiting the number of strongly associated genes when studying susceptibility to the disease. The goal of this study was to compare computational tools with and without feature selection for predicting osteoporosis outcome in Taiwanese women based on genetic factors such as single nucleotide polymorphisms (SNPs). To elucidate relationships between osteoporosis and SNPs in this population, three classification algorithms were applied: multilayer feedforward neural network (MFNN), naive Bayes, and logistic regression. A wrapper-based feature selection method was also used to identify a subset of major SNPs. Experimental results showed that the MFNN model with the wrapper-based approach was the best predictive model for inferring disease susceptibility based on the complex relationship between osteoporosis and SNPs in Taiwanese women. The findings suggest that patients and doctors can use the proposed tool to enhance decision making based on clinical factors such as SNP genotyping data.

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An ANFIS-based model for predicting adequacy of vancomycin regimen using improved genetic algorithm

Cited by 27 publications

References 20 publications

Mortality Predicted Accuracy for Hepatocellular Carcinoma Patients with Hepatic Resection Using Artificial Neural Network

Mortality Predicted Accuracy for Hepatocellular Carcinoma Patients with Hepatic Resection Using Artificial Neural Network

A novel hybrid ICA-ANFIS model for prediction of manufacturing processes performance

Comparison of Classification Algorithms with Wrapper-Based Feature Selection for Predicting Osteoporosis Outcome Based on Genetic Factors in a Taiwanese Women Population

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