A Genetic Algorithm to Improve a Neural Network to Predict a Patient’s Response to Warfarin
A Genetic Algorithm to Improve a Neural Network to Predict a Patient’s Response to Warfarin
复制标题
一种改进神经网络的遗传算法来预测患者对华法林的反应
DOI：
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发表时间：
1993
影响因子：
1.7
通讯作者：
S. Lucas
中科院分区：
文献类型：
--
作者：
M. Narayanan;S. Lucas
Abstract: The ability of neural networks to predict the international normalised ratio (INR) for patients treated with Warfarin was investigated. Neural networks were obtained by using all the predictor variables in the neural network, or by using a genetic algorithm to select an optimal subset of predictor variables in a neural network. The use of a genetic algorithm gave a marked and significant improvement in the prediction of the INR in two of the three cases investigated. The mean error in these cases, typically, reduced from 1.02 ± 0.29 to 0.28 ± 0.25 (paired t-test, t = −4.71, p <0.001, n = 30). The use of a genetic algorithm with Warfarin data offers a significant enhancement of the predictive ability of a neural network with Warfarin data, identifies significant predictor variables, reduces the size of the neural network and thus the speed at which the reduced network can be trained, and reduces the sensitivity of a network to over-training.
