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1. Problem analysis
Analyzing the impact of different treatments on edema volume progression patterns requires in-depth data analysis and modeling.
First, relevant fields need to be extracted from the data set, including treatment methods and changes in edema volume. Ensure data quality, including handling missing values and outliers. Perform feature engineering to extract or transform data features to facilitate analysis. This can include coding treatments to facilitate model use.
Use appropriate machine learning or statistical models to study the impact of treatments on edema volume progression. Here we can choose a suitable regression model such as decision tree regression or random forest regression.
Once the model is built, we can use permutation importance techniques to evaluate the impact of different features on the model's predictions. The basic idea of this technique is to randomly replace a feature (disrupt its relationship with the target) and then observe the changes in model performance.
First, record the performance of the original model on the test data, typically using mean squared error or other appropriate performance metric. Next for each treatment feature, it is permuted and model performance is re-evaluated. Calculate the performance index after replacement.
Compare original performance with permutation performance. If a treatment characteristic has a significant impact on model performance, then performance will decrease after permutation, resulting in a worse model fit.
Based on the differences in performance of the different treatment characteristics, we can quantify their impact on edema volume progression. Features with reduced performance after replacement may have a greater impact on edema volume, whereas features with little change in performance may have a smaller impact on progression of edema volume.
Report the results of the analysis clearly, including which treatment characteristics have a significant impact on edema volume progression, their replacement importance values, and possible causes or explanations.