/Cardiovascular-Disease-Classification

MICCAI Dataset Segmentation: Implemented a 2D UNet architecture to achieve 96.38% accuracy in segmenting cardiovascular structures from MICCAI medical images. Patient Classification: Employed Gradient Boosting Machines (GBM) and Random Forest (RF) algorithms to classify patients into five groups with 92.76% accuracy on the testing set.

Primary LanguageJupyter Notebook

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