Table 4 Performance metrics of machine learning models for cancer risk prediction based on lifestyle and genetic data.
From: Predicting cancer risk using machine learning on lifestyle and genetic data
Model | Test accuracy | Test precision | Test recall | Test F1-score |
|---|---|---|---|---|
LR | 0.8583 | 0.7865 | 0.8235 | 0.8046 |
k-NN | 0.8875 | 0.9531 | 0.7176 | 0.8188 |
SVM | 0.9250 | 0.8941 | 0.8941 | 0.8941 |
DT | 0.9333 | 0.9059 | 0.9059 | 0.9059 |
RF | 0.9667 | 0.9753 | 0.9294 | 0.9518 |
GB | 0.9750 | 0.9759 | 0.9529 | 0.9643 |
XGBoost | 0.9750 | 0.9647 | 0.9647 | 0.9647 |
LightGBM | 0.9750 | 0.9759 | 0.9529 | 0.9643 |
CatBoost | 0.9875 | 1.0000 | 0.9647 | 0.9820 |
