Breast cancer survival analysis and machine learning to predict the impact of different treatments

Abstract

Breast cancer is the most common form of cancer among women, impacting approximately one million women worldwide. New treatments are being developed yearly, improving breast cancer patients' survival rates. To explore the impact of different treatments, we conducted this study using data from the Surveillance, Epidemiology, and End Results (SEER) database. The study employed Kaplan-Meier analysis to examine breast cancer-specific survival (BCSS) and overall survival (OS) rates across various treatment options, including ‘chemotherapy’, ‘radiotherapy, ‘both therapies’, and ‘no therapy’. The log-rank test was also utilized to assess the statistical significance of differences observed between multiple survival curves. We found that recommended treatment for most breast cancer cases, based on BCSS analysis, is the combination of ‘both’ chemotherapy and radiotherapy. On the other hand, according to OS analysis, ‘radiotherapy only’ or ‘in conjunction with chemotherapy’ is the superior treatment for most breast cancer cases. They are often preferred over ‘chemotherapy only’ for most breast cancer patients. Machine learning was used to develop ten models predicting the survivability for OS and BCSS. C5.0 algorithm consistently achieves strong overall performance. It achieves high accuracy 0.98 and sensitivity of 0.99 for both OS and BCSS, reasonably RMSE of (0.14, 0.15 for BCSS and OS, respectively), and good ROC score of (0.91, 0.88 for BCSS and OS, respectively).