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

Authors:
Wen-Hsien Ho;Jian-Xun Chen;I-Nong Lee;Hui-Chen Su
Affiliations:
Department of Medical Information Management, Kaohsiung Medical University, 100 Shin-Chuan 1st Road, Kaohsiung 807, Taiwan, ROC;Department of Information Management, Chang Jung Christian University, 396 Chang Jung Road, Sec. 1, Kway Jen, Tainan 711, Taiwan, ROC;Department of Medical Information Management, Kaohsiung Medical University, 100 Shin-Chuan 1st Road, Kaohsiung 807, Taiwan, ROC;Department of Pharmacy, Chi Mei Medical Center, 901 Chung Hwa Road, Yong Kang, Tainan 701, Taiwan, ROC
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

Citing 7
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Abstract

In this paper, a model based on the adaptive network-based fuzzy inference system (ANFIS) with the improved genetic algorithm is used to predict the adequacy of vancomycin regimen. The improved genetic algorithm, i.e., hybrid Taguchi-genetic algorithm (HTGA), is applied in the ANFIS to simultaneously find the optimal premise and consequent parameters and a total output layer parameter by directly 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. Therefore, this study demonstrates the feasibility of applying the HTGA-based ANFIS as the mechanism of the decision support systems for the adequacy of vancomycin regimen for the patients based on clinical databases.