Study on microwave ablation temperature prediction model based on grayscale ultrasound texture and machine learning
Fig 5
Ranking of features by importance in the 15 W and 20 W ablation groups.
This figure displays the importance ranking of temperature prediction features identified by the random forest algorithm, based on their contribution to predicting temperature changes. It highlights the features with the greatest predictive value during the microwave ablation process.