Dr. Freshteh OsmaniUniversity of Birjand, Iran
Title: Comparison of breast cancer recurrence prediction by random forest, artificial neural network and Cox regression modols
Breast cancer is one of the most common cancers, and also it is the most common type of malignancy in Iranian women that has been growing in recent years. In patients with this disease, there is usually the risk of recurrence. Many factors increase or decrease the risk of recurrence. A variety of models that can be noted to detect and predict breast cancer recurrence and factors affecting it, classical statistical methods such as Cox Regression and new methods such as Artificial Neural Network and Random Forest. In this study, using these three techniques, recurrence of breast cancer was predicted and factors affecting relapse were detected. Models and prediction Accuracy are compared using ROC curve. In this retrospective study, also called historic cohort study, 342 patients with breast cancer with 13 features from each patient were used.
Data of patients in the Cancer Research Center of Shohada Tajrish hospital, have been recorded to track, and patients for at least 6 months after diagnosis were being monitored and follow-up was done for them. To develop predictive models of breast cancer recurrence and determination of factors affecting recurrence, the three-layer Perceptron Artificial Neural Network algorithms, Random Forest classification and Cox Regression models were fitted. Area under the ROC for the Artificial Neural Network, Random Forest and Cox Regression obtained 87%, 76% and 65%, respectively. Artificial Neural Network model classification accuracy is obtained 80%, against 60% for random forest model and 33% for cox regression model. So, ANN model predicted better than the two other models.
Freshteh Osmani completed his Ph.D. at the age of 32 years from Tarbiat Modares University, Tehran, Iran. She is an assistant professor of biostatistics and the head of the Student Research Committee of the dentistry department. She has over 70 publications that have been cited over 250 times, and his publication h-index is 11. He has been serving as an editorial board and reviewer member of several journals.