为什么我的重要模型给我的 AUC 和 ROC 很低？

Question

出于某种原因，在通过 混淆矩阵 和 AUC ROC 检查其准确性时，我的模型一直显示为较差的模型。这是我在进行向后消除后坚持使用的模型。这是逻辑输出：

`Call:
glm(formula = DEATH_EVENT ~ age + ejection_fraction + serum_sodium +
    time, family = binomial(link = "logit"), data = train, control = list(trace = TRUE))

Deviance Residuals:
    Min       1Q   Median       3Q      Max
-2.1760  -0.6161  -0.2273   0.4941   2.6827

Coefficients:
                   Estimate Std. Error z value Pr(>|z|)
(Intercept)       15.741338   7.534348   2.089  0.03668 *
age                0.063767   0.018533   3.441  0.00058 ***
ejection_fraction -0.080520   0.019690  -4.089 4.33e-05 ***
serum_sodium      -0.111499   0.053639  -2.079  0.03765 *
time              -0.020543   0.003331  -6.167 6.95e-10 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1`

这是混淆矩阵输出

glm.pred Survived Dead
   0       46   10
   1        5   14

auc 显示为 0.178

library(pROC)

# Calculate predicted probabilities for test set
glm.probs <- predict(glm9, newdata=test, type="response")

# Create prediction object for test set
pred <- prediction(glm.probs, test$DEATH_EVENT)

# Create ROC curve for test set
roc.perf <- performance(pred, measure = "tpr", x.measure = "fpr")

# Plot ROC curve for test set
plot(roc.perf, legacy.axes = TRUE, percent = TRUE,
     xlab = "False Positive Percentage", ylab = "True Positive Percentage",
     col = "#3182bd", lwd = 4, print.auc = TRUE)

# Add AUC to ROC curve
auc <- as.numeric(performance(pred, measure = "auc")@y.values)
text(x = 0.5, y = 0.3, labels = paste0("AUC = ", round(auc, 3)),
     col = "black", cex = 1.5)
abline(a=0, b= 1)

我怎样才能解决这个问题？

我检查了类，它显示存在数据不平衡。但我不知道如何处理这些知识。