Plotting ROC curve after re-sampling the data set

I am a beginner in Machine Learning. I am working on a project to compare the performance of three ML algorithms which are: LR, SVM and ANN on a dataset containing medical records.

After exploring the data I have found that the dataset is imbalanced, and hence I have applied SMOTE. Then I applied PCA on the resampled data to reduce the dimensionality of the dataset and to improve the performance of the Algorithms.

After plotting a heatmap of the Confusion Matrices of each Algorithms I want to plot the ROC curves showing the AUC scores. Following is the screenshot of the heatmap of the Confusion Matrix of SVM.

And this is the code that I have used to plot the ROC Curve

    from sklearn.metrics import roc_curve, auc
### Fit a sklearn classifier on train dataset and output probabilities
pred_val = svc.predict_proba(self.X_test)[:,1]
### Compute ROC curve and ROC area for predictions on validation set
fpr, tpr, _ = roc_curve(self.y_test, pred_val)
roc_auc = auc(fpr, tpr)

### Plot
plt.figure()
lw = 2
plt.plot(fpr, tpr, color='darkorange',
lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)
plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.legend(loc="lower right")
plt.show()


This is the result:

Please tell me wheter the result shown by the ROC curve is correct or not.