How to Display ROC plot

Hi guys, i'm using 10 times k fold cross validation for the implementation of machine learning.. i already display the confusion matrix, but i would like to display the ROC plot

Here's the example of my code..

function NB=jNB(feat,label,Dist,kfold)

switch Dist

case'n'; Dist='normal';

case'k'; Dist='kernel';

end

rng('default');

% Divide data into k-folds

fold=cvpartition(label,'kfold',kfold);

% Pre

pred2=[]; ytest2=[]; Afold=zeros(kfold,1);

% Naive Bayes start

for i=1:kfold

% Call index of training & testing sets

trainIdx=fold.training(i); testIdx=fold.test(i);

% Call training & testing features and labels

xtrain=feat(trainIdx,:); ytrain=label(trainIdx);

xtest=feat(testIdx,:); ytest=label(testIdx);

% Training the model

Model=fitcnb(xtrain,ytrain,'Distribution',Dist);

% Perform testing

Pred0=predict(Model,xtest); %A=size(Pred0,1);

% Confusion matrix

con=confusionmat(ytest,Pred0);

disp(['The Confusion matrix for fold number ' num2str(i)])

disp(con)

% Accuracy for each fold

Afold(i)=(sum(diag(con))/sum(con(:)))*100;

%Sensitivity for each fold

TP=con(1);

TP_FN=sum(con(:,1));

sensitivity=(TP/TP_FN)*100;

sensitivityAll(i)=(TP/TP_FN)*100;

disp(['Classification Sensitivity For Each Fold(NB) ' num2str(i)])

disp(sensitivity)

%Specificity for each fold

TN=con(4);

TN_FP=sum(con(:,2));

specificity=(TN/TN_FP)*100;

specificityAll(i)=(TN/TN_FP)*100;

disp(['Classification Specificity For Each Fold(NB) ' num2str(i)])

disp(specificity)

% Store temporary

pred2=[pred2(1:end);Pred0]; ytest2=[ytest2(1:end);ytest];

end

%Sensitivity Average

bcc=mean(sensitivityAll);

disp('Classification Sensitivity Average(NB)')

disp(bcc)

%Specificity Average

ccc=mean(specificityAll);

disp('Classification Sensitivity Average(NB)')

disp(ccc)

% Overall confusion matrix

confmat=confusionmat(ytest2,pred2);

% Average accuracy over k-folds

acc=mean(Afold);

% Store results

NB.fold=Afold; NB.acc=acc; NB.conf=con; NB.con=confmat; NB.bcc=bcc; NB.ccc=ccc;

fprintf('\n Classification Accuracy For Each Fold(NB): %g %%',Afold);

fprintf('\n Classification Average Accuracy (NB): %g %%',acc);

fprintf('\n Classification Average Sensitivity (NB): %g %%',bcc);

fprintf('\n Classification Average Specificity (NB): %g %%',ccc);

fold

disp(NB.conf);

end

with that code i can display all confusion matrix. but i want to display the ROC plot based on my code. i do really appreciate your help and your answer.. Thank You

[arun anoop m]

https://in.mathworks.com/matlabcentral/fileexchange/71461-simple-machine-learning-algorithms-for-classification?s_tid=prof_contriblnk