Comparison of the Performance of Log-logistic Regression nd Artificial Neural Networks for Predicting Breast Cancer elapse

Background: Breast cancer is the most common cancers in female populations. The exact cause is notknown, but is most likely to be a combination of genetic and environmental factors. Log-logistic model (LLM)is applied as a statistical method for predicting survival and it influencing factors. In recent decades, artificialneural network (ANN) models have been increasingly applied to predict survival data. The present research wasconducted to compare log-logistic regression and artificial neural network models in prediction of breast cancer(BC) survival. Materials and
Methods: A historical cohort study was established with 104 patients sufferingfrom BC from 1997 to 2005. To compare the ANN and LLM in our setting, we used the estimated areas underthe receiver-operating characteristic (ROC) curve (AUC) and integrated AUC (iAUC). The data were analyzedusing R statistical software.
Results: The AUC for the first, second and third years after diagnosis are 0.918, 0.780and 0.800 in ANN, and 0.834, 0.733 and 0.616 in LLM, respectively. The mean AUC for ANN was statisticallyhigher than that of the LLM (0.845 vs. 0.744). Hence, this study showed a significant difference between theperformance in terms of prediction by ANN and LLM.
Conclusions: This study demonstrated that the abilityof prediction with ANN was higher than with the LLM model. Thus, the use of ANN method for prediction ofsurvival in field of breast cancer is suggested.