Table 3.
The best feature combinations that yield the highest predictive accuracy of statistical models for heart toxicity‐based classification. Features that are consistently selected by all the models are bold
Nearest neighbors | Linear SVM | RBF SVM | Decision Trees | Random Forest | Ada‐boost | Naive Bayes | Linear DA | Quadratic DA | |
---|---|---|---|---|---|---|---|---|---|
Selected Features | VolB | VolB | VolB | VolB | VolB | VolB | VolB | VolB | VolB |
DisH‐B | Thickness | DisH‐B | DisH‐B | VolH | |||||
VolipsL | |||||||||
DisH‐B | |||||||||
Laterality | |||||||||
Thickness | |||||||||
Accuracy | 0.83 | 0.77 | 0.83 | 0.73 | 0.77 | 0.73 | 0.83 | 0.83 | 0.93 |
VolB: Breast Volume; VolH: Heart Volume; VolipsL: IpsiLung Volume; DisH‐B: Distance between Breast and Heart; Thickness: Deep breath motion thickness variation.