ezgisubasi/breast-cancer-gene-expression
This project aims to predict people who will survive breast cancer using machine learning models with the help of clinical data and gene expression profiles of the patients.
Integrates 331 mRNA expression profiles and 175 gene mutations across 1,904 patient records with 31 clinical attributes. Uses PCA for dimensionality reduction (375 components for 95% variance) and evaluates multiple scikit-learn classifiers, with Logistic Regression achieving 89% test accuracy. Applies label and one-hot encoding for categorical preprocessing while analyzing gene mutations (BRCA1/BRCA2, TP53, ATM) associated with hereditary breast cancer risk.
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Jun 27, 2021
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