WASET
	@article{(Open Science Index):https://publications.waset.org/pdf/16509,
	  title     = {Comparison of Neural Network and Logistic Regression Methods to Predict Xerostomia after Radiotherapy},
	  author    = {Hui-Min Ting and  Tsair-Fwu Lee and  Ming-Yuan Cho and  Pei-Ju Chao and  Chun-Ming Chang and  Long-Chang Chen and  Fu-Min Fang},
	  country	= {},
	  institution	= {},
	  abstract     = {To evaluate the ability to predict xerostomia after
radiotherapy, we constructed and compared neural network and
logistic regression models. In this study, 61 patients who completed a
questionnaire about their quality of life (QoL) before and after a full
course of radiation therapy were included. Based on this questionnaire,
some statistical data about the condition of the patients’ salivary
glands were obtained, and these subjects were included as the inputs of
the neural network and logistic regression models in order to predict
the probability of xerostomia. Seven variables were then selected from
the statistical data according to Cramer’s V and point-biserial
correlation values and were trained by each model to obtain the
respective outputs which were 0.88 and 0.89 for AUC, 9.20 and 7.65
for SSE, and 13.7% and 19.0% for MAPE, respectively. These
parameters demonstrate that both neural network and logistic
regression methods are effective for predicting conditions of parotid
glands.
},
	    journal   = {International Journal of Biomedical and Biological Engineering},
	  volume    = {7},
	  number    = {7},
	  year      = {2013},
	  pages     = {413 - 417},
	  ee        = {https://publications.waset.org/pdf/16509},
	  url   	= {https://publications.waset.org/vol/79},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 79, 2013},
	}