Resumen
Multi-gene assays (MGAs), such as Oncotype DX and Mammaprint, are used to provide predictive and prognostic values in treatment of ER+HER2- breast cancer. However, their accessibility is restricted due to their high cost in some countries. For this reason, many studies have been conducted to develop the tests that can replace the multi-gene assays, but practicality is still insufficient. The aim of our study is to develop a highly accessible machine learning-based model for predicting the result of MGA. Our accurate and affordable machine learning-based predictive model may serve as a cost-effective alternative to the expensive multi-gene assays.