Breast Cancer Survival Prediction Using Machine Learning and Deep Learning

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Authors
Roy, Marteena
Issue Date
2023-08
Degree
MSc in Data Analytics
Publisher
Dublin Business School
Rights
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Abstract
Breast cancer continues to be a highly widespread and formidable kind of malignancy on a global scale. The precise estimation of survival is of utmost importance in order to customize treatment plans and enhance patient outcomes. The objective of this study was to improve the accuracy of breast cancer survival prediction by combining two well-known datasets: METABRIC, which provides extensive molecular profiling data, and CBIS-DDSM, a valuable collection of mammographic images. The study utilized three sophisticated machine learning models, namely Inception Net, Adam Net, and DenseNet, to make the most of how molecular and imaging information can work together effectively. The DenseNet model stood out, achieving an 81% accuracy in predicting breast cancer patient survival, surpassing Inception Net (66%) and Adam Net (71%). We can create a helpful tool using DenseNet121 to study how well breast cancer patients might survive by looking at their mammograms.