Feature selection for SVM classifier

I am trying to have matlab do a feature selection for me so I can use the svm classifier on my data and check the ideal performance for each amount of features used for the classification. In my script, I have checked the differentiation between the two groups ("healthy" and "sick") through t-statistics. The t-statistics actually already show me which features would be best, since the feature with the lowest p-value would have the best discriminating properties, but I want it to be done by the sequentialfs command. The problem is, that the feature selection selects different genes than I would have chosen when looking at the p-values (my first-choice feature would be A and the feature selection selects B). Could anyone check if there is something wrong with either the t-statistics or the feature selection? I have attached the dataset matrix to this message. Any help is greatly appreciated!

load samples1

ID=samples1(:,12)

ID(ID<3)=0

ID(ID>=3)=1

samples1(:,13)=ID

%% Determining significancy of feature differentiation between sick and healthy group

sick=find(samples1(1:60,12)>=3);

healthy=find(samples1(1:60,12)<3);

sick2 = samples1(sick,:);

healthy2 = samples1(healthy,:);

[h,p,ci,stats] = ttest2(healthy2,sick2);

%% Train/Test Division

%

x_train=(samples1(1:60,2:7))

y_train=(samples1(1:60,13))

x_test=(samples1(61:end,2:7))

y_test=(samples1(61:end,13))

%% CV partition

c=cvpartition(y_train,'LeaveOut')

%% feature selection

opts = statset('display','iter');

classf = @(x_train, y_train, x_test, y_test)...

sum(predict(fitcsvm(x_train, y_train,'KernelFunction','RBF','Kernelscale','auto'), x_test)~=y_test);

[fs, history] = sequentialfs(classf, x_train, y_train, 'cv', c, 'options', opts,'nfeatures',6);

%% Best hyperparameter

X_train_w_best_feature = x_train(:,fs);

Mdl = fitcsvm(X_train_w_best_feature,y_train,'KernelFunction','rbf','OptimizeHyperparameters','auto',...

'HyperparameterOptimizationOptions',struct('AcquisitionFunctionName',...

'expected-improvement-plus','ShowPlots',true)); % Bayes' Optimization.

%% Final test with test set

X_test_w_best_feature = x_test(:,fs);

test_accuracy_for_iter = sum((predict(Mdl,X_test_w_best_feature) == y_test))/length(y_test)*100

%% Extract error rate

label = predict(Mdl, X_test_w_best_feature)

L=loss(Mdl,X_test_w_best_feature,y_test)