Cancer research, including that of breast cancer, has increasingly relied on molecular profiling based on advances in genomic technology. Although these techniques have permitted scientists to unravel the process by which cancer develops, scientists still struggle to effectively translate the vast amounts of patient data into clinically meaningful results. As a result, tasks such as predicting the human response to differing treatments remains a major challenge in cancer treatment. There have been many studies attempting to determine the survival indicators of breast cancer patients. However, most of these analyses were predominantly performed using traditional statistical methods, which are imperfect and inadequate in tackling vast amounts of data or unstructured data on human breast cancer. With the exponential progress in computing power and artificial intelligence approaches, we believe that there is an opportunity for machine learning to supersede our current capabilities in fully understanding the correlations between geneset alterations, drug responses, and the prognosis of breast cancer patients. This information would greatly benefit scientists and physicians in developing clinical therapeutic strategies, such as performing personalized treatment. This machine learning project employs multiple machine learning approaches, including a novel deep learning algorithm, in building models for the detection and visualization of significant prognostic indicators of breast cancer patient survival rate. The clinical and genomic data of 1,980 primary breast cancer samples used in this project were obtained from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) database of cBioPortal. The data was preprocessed and then split to train eight classical machine learning models and the aforementioned deep learning Convolutional Neural Network (CNN) model. These models were evaluated using the recall scores, the accuracy scores, the receiver operating characteristic (ROC) curve, and the area under the ROC curve (AUC) on the training dataset and confirmed using the rest of the data of the dataset. Both the deep learning and machine learning methods produced desirable prediction accuracies. However, the deep learning model noticeably outperformed all other classifiers and achieved the highest accuracy (AUC = 0.900). This project was constructed in the Google Colab environment based on python and its programming libraries with data visualization, Tensorflow, and Keras. The CNN model demonstrates a powerful ability to be used as a systematic framework for real time prediction by end users. A web application for the breast cancer survival rate prediction was designed and developed using streamlit, Tensorflow, Keras and python libraries to allow end-users to interact with the model with ease and obtain quick and accurate prediction.

The authors have declared no competing interest.

This study did not receive any funding

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Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) database of cBioPortal

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