Abstract
Breast cancer is the most common cancers in female populations. The exact cause is not known, 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, artificial neural network (ANN) models have been increasingly applied to predict survival data. The present research was conducted to compare log-logistic regression and artificial neural network models in prediction of breast cancer (BC) survival. A historical cohort study was established with 104 patients suffering from BC from 1997 to 2005. To compare the ANN and LLM in our setting, we used the estimated areas under the receiver-operating characteristic (ROC) curve (AUC) and integrated AUC (iAUC). The data were analyzed using R statistical software. The AUC for the first, second and third years after diagnosis are 0.918, 0.780 and 0.800 in ANN, and 0.834, 0.733 and 0.616 in LLM, respectively. The mean AUC for ANN was statistically higher than that of the LLM (0.845 vs. 0.744). Hence, this study showed a significant difference between the performance in terms of prediction by ANN and LLM. This study demonstrated that the ability of prediction with ANN was higher than with the LLM model. Thus, the use of ANN method for prediction of survival in field of breast cancer is suggested.
Highlights
Breast cancer (BC) is one of the most common cancers in female population and the most important cause of death due to cancer in female’s worldwide (Jemal et al, 2011; National cancer institute, 2012; WHO, 2012)
This study demonstrated that the ability of prediction with artificial neural network (ANN) was higher than with the Log-logistic model (LLM) model
The results showed that the performances of ANN and LLM were statistically different (p
Summary
Breast cancer (BC) is one of the most common cancers in female population and the most important cause of death due to cancer in female’s worldwide (Jemal et al, 2011; National cancer institute, 2012; WHO, 2012). The exact cause of breast cancer is not known, but is most likely to be a combination of genetic and environmental factors. About 23% of total new cancer cases and 14% of the cancer death in females in 2008 are due to breast cancer (Jemal et al, 2011) Many risk factors such as family history, age, tumor size, early diagnosis are known to play important a role in pathogenesis of metastasis and death in BC patients (Faradmal et al, 2010). Log-logistic model (LLM) is applied as a statistical method for predicting survival and it influencing factors. The present research was conducted to compare log-logistic regression and artificial neural network models in prediction of breast cancer (BC) survival. The use of ANN method for prediction of survival in field of breast cancer is suggested
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