Cerebrovascular disease refers to a group of conditions that affect the blood vessels and blood supply to the brain. This includes conditions such as stroke, transient ischemic attack (TIA), and other disorders that affect the brain's blood circulation. Early detection and prediction of cerebrovascular disease risk is crucial for effective treatment and prevention of complications.
To address this issue, we use Singa to implement a machine learning model for predicting cerebrovascular disease. The model uses tabular data with various clinical features to predict the likelihood of cerebrovascular disease.
The dataset used in this task is MIMIC-III after preprocessed. Before starting to use this model for cerebrovascular disease prediction, download the sample dataset for cerebrovascular disease prediction: https://github.com/lzjpaul/singa-healthcare/tree/main/data/cerebrovascular
data includes the scripts for preprocessing Cerebrovascular datasets.
model includes the MLP model construction codes by creating a subclass of Module to wrap the neural network operations of each model.
train.py is the training script, which controls the training flow by doing BackPropagation and SGD update.
python train.py cerebrovascularnet -dir pathToDataset