Article directory
1. Problem analysis
According to the problem statement, our goal is to predict whether a patient will experience a hematoma expansion event. We will use the personal history, disease history, onset and treatment-related characteristics of the first 100 patients, as well as their imaging examination results and other characteristics to construct a model to predict the probability of hematoma expansion in all patients.
First, the data is preprocessed, including missing value processing, feature encoding, data normalization or standardization, etc., to make the data suitable for model training.
Before building a model, features need to be selected. This may involve filtering, dimensionality reduction or transformation of features to ensure the model has the most relevant output.