Constructing of the risk classification model of cervical cancer by artificial neural network
It is significant to build up the risk classification model of cervical cancer for the evaluation of high-risk population. Data were divided into two sub-data, one is model building sub-data, the other is model testing sub-data. By using of artificial neural network (ANN) analysis method (Back Propagation, BP), the risk classification model had been setup. The parameters were listed as following: the data had been treated as normalization, and the level of network was 3, and the number of neural in hidden level was 5, and the transmitting function between input level and hidden level was logsig, and the transmitting function between hidden level and output level was purelin, and the studying method was LevenbergMarquardt optimizing, and the error parameter eg=0.09, maximum epochs me=8000. The model quality was good (sensitivity=98%, specificity=97%), and the back calculation fitting result was excellent. The predictive value of 10 unknown data was also good, during which the correct rate of control group was 100%, and that of case group was 80%. Because ANN is with the character of self-organizing, self-learning and self-adapting, the ANN risk classification model is fit for the screening of high-risk population of local cervical cancer, risk evaluation of cervical cancer and the effect evaluation of the prevention method after training the model by new data of some area.
model cervical cancer artificial neural network (ann)