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Predicting Pregnant Women's Abortion: Artificial Neural Network, Wavelet Neural Network and Adaptive Neural Fuzzy
Amir Hossein Hashemian1, Behzad Mahaki1,2, Mansour Rezaei1
1Department of Biostatistics, School of Health, Kermanshah University of Medical Sciences, Kermanshah, Iran.
Background:
Abortion is an important and controversial issue and one of the important reasons for the mortality of pregnant women worldwide. This study aimed to predict the risk factors of abortion in pregnant women using artificial neural network, wavelet neural network, and adaptive neural fuzzy inference system.
Materials And Methods:
The study is an analytical-comparative modeling and data of 4437 pregnant women from the Ravansar Non-Communicable Disease (RaNCD) cohort study from 2014 to 2016 was used. First, six variables were chosen through the genetic algorithm approach, then artificial neural network (ANN), wavelet neural network (WNN), and adaptive neural fuzzy inference system (ANFIS) were run. Finally, the performance of the models was compared based on the evaluation criteria. All analyses were done in MATLAB R2019b software.
Results:
ANN with RMSE of 0.019 showed better performance than ANFIS and WNN with 0.42 and 1.445, respectively. Further, the accuracy, sensitivity, and specificity in ANN were 100%, 99%, and 100%, while in WNN, they were 76.2%, 76.4%, and 66.7%. However, when the researchers used three selected variables, the accuracy, sensitivity, and specificity as well as RMSE in ANFIS were 100%, 100% 100%, and 0; 100%, 99%, 100%, and 0.021 in ANN; and finally 76.2%, 76.4%, 38.5%, and 1.553 in WNN.
Conclusion:
The models with six input variables indicated that the artificial neural network has a better performance than the other two models, but based on the three variables, the fuzzy neural inference system performed better than the other two models.
