Machine Learning based Breast Cancer Prognosis Analysis on Multi-Source Big Data

Breast cancer is the most common cancer among women worldwide with a high recurrence rate. The prognosis estimation of breast cancer remains a challenge, which is only investigated by a few works. In our study, we apply various machine learning methods on multi-source breast cancer big data for feature analysis and prognosis evaluation. Based on our experimental results, we conclude that the implementation of machine learning algorithms helps to extract prognostic information, while strategic feature engineering and multi-source data fusion demonstrate significant potential to enhance prognosis prediction performance and clinical decision-making processes.