Abstract
With the rise of deep learning, cancer-specific survival prediction is a research topic of high interest. There are many benefits to both patients and caregivers if a patient’s survival period and key factors to their survival can be acquired early in their cancer journey. In this study, we develop survival period prediction models and conduct factor analysis on data from breast cancer patients (Surveillance, Epidemiology, and End Results (SEER)). Three deep learning architectures - Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) are selected for modeling and their performances are compared. Across both the classification and regression approaches, deep learning models significantly outperformed traditional machine learning models. For the classification approach, we obtained an 87.5% accuracy and for the regression approach, Root Mean Squared Error of 13.62% and \({R}^{2}\) value of 0.76. Furthermore, we provide an interpretation of our deep learning models by investigating feature importance and identifying features with high importance. This approach is promising and can be used to build a baseline model utilizing early diagnosis information. Over time, the predictions can be continuously enhanced through inclusion of temporal data throughout the patient’s treatment and care.
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Data Availability
The SEER cancer registry is made available through the NCI and the process to access the data along with the documentation is provided at https://seer.cancer.gov/data/.
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Acknowledgement
The authors of this work would like to acknowledge the NSF I/UCRC Center for Healthcare Organization Transformation (CHOT), NSF I/UCRC award #1624727 and in part by Susan G. Komen Foundation for funding this research. Any opinions, findings, or conclusions found in this paper are those of the authors and do not necessarily reflect the views of the sponsors.
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Doppalapudi, S., Yang, H., Jourquin, J., Qiu, R.G. (2021). Deep Learning and Prediction of Survival Period for Breast Cancer Patients. In: Qiu, R., Lyons, K., Chen, W. (eds) AI and Analytics for Smart Cities and Service Systems. ICSS 2021. Lecture Notes in Operations Research. Springer, Cham. https://doi.org/10.1007/978-3-030-90275-9_1
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