A Comparative Analysis of Deep Learning Approaches for Predicting Breast Cancer Survivability

Breast cancer is the second largest cause of mortality among women. Breast cancer patients in developed nations have a relative survival rate of more than 5-years due to early detection and treatment. Deep learning approaches can help enhance the identification of breast cancer cells, lower the risk of detection mistakes, and minimize the time it takes to diagnose breast cancer using human methods. This paper examines the accuracy of artificial neural networks, Restricted Boltzmann Machine, Deep Autoencoders, and Convolutional Neural Networks (CNN) for post-operative survival analysis of breast cancer patients. A thorough examination of each network's operation and design is carried out to determine which network outperforms the other, followed by an analysis based on the network's prediction accurateness. The experimental results assert that all the deep learning techniques can predict the survival of breast cancer patients. The accuracy score achieved by Restricted Boltzmann Machine performed is the highest (0.97), followed by deep Autoencoders that attained an accuracy score of 0.96. CNN achieved a 92% accuracy score, while artificial neural networks attained the least accuracy score (0.89). The prediction performance of models has been evaluated using distinct parameters like accuracy, the area under the curve, F1 Score, Matthew’s correlation coefficient, sensitivity, and specificity. Also, the models have been validated using fivefold cross-validation techniques. However, there is still a need for complete analysis and research using deep learning methods to determine the design that provides superior accuracy.