Specifity, sensibility and confusion matrix of a k-fold

I'd like to calculate the sensitivity and specificity of my model and display the confusion matrix and i really don't know how because I used a k-fold of 5. Can anyone help me pleaaaase ? here is my code ?

clear variables;
close all;
clc;
%%
%preparing dataset
echantillon=readtable('melanome_naevus.xlsx');
classe=table2cell(echantillon(:,1));
y=grp2idx(classe);
X=table2array(echantillon(:,2:end));
%%
%divide database into test and train
rand_num = randperm(size(X,1));
X_train = X(rand_num(1:round(0.8*length(rand_num))),:);
y_train = y(rand_num(1:round(0.8*length(rand_num))),:);

X_test = X(rand_num(round(0.8*length(rand_num))+1:end),:);
y_test = y(rand_num(round(0.8*length(rand_num))+1:end),:);
%% CV partition

c = cvpartition(y_train,'kFold',5);
%% feature selection
opts = statset('display','iter');
classf = @(train_data, train_labels, test_data, test_labels)...
    sum(predict(fitcsvm(train_data, train_labels,'KernelFunction','Polynomial'),...
    test_data) ~= test_labels);
[fs, history] = sequentialfs(classf, X_train, y_train, 'cv', c, 'options', opts);
%% Best hyperparameter

X_train_w_best_feature = X_train(:,fs);

Md1 = fitcsvm(X_train_w_best_feature,y_train,'KernelFunction','Polynomial',...
'OptimizeHyperparameters','auto','HyperparameterOptimizationOptions',...
struct('AcquisitionFunctionName','expected-improvement-plus','ShowPlots',true));
%% Final test with test set
X_test_w_best_feature = X_test(:,fs);
test_accuracy_for_iter = sum((predict(Md1,X_test_w_best_feature) == y_test))/length(y_test)*100