How to train SVM that contains data with NAN or 0 values

Hello Matlab experts,

I need some help with the following. I am using SVM and in my feature extraction I obtain area of abnormality detected in the retina fundus. And when no abnormalities are detected, the area will be 0 and also the density ratio which is also an attribute used to train my SVM. But now Matlab gives the Error using svd Input to SVD must not contain NaN or Inf.

Here is my code: what must I do????? This is the data I load.

1 66 0.91667 -1

0 0 0 1

0 0 0 1

0 0 0 1

2 182 0.54167 -1

1 1 Inf 1

MyData=load ('My_69_Fotos.csv')

% Vir My_69_fotos groups=[1;1;1;1;1;1;1;1;1;1;1;1;1;1;1;1;1;...

    1;1;1;1;1;0;0;0;1;0;1;0;0;1;0;...

    0;0;0;1;0;0;0;0;0;0;1;0;0;0;...

    0;0;0;1;1;1;1;0;0;1;1;0;0;0;0;0;0;...

    0;0;0;1;1;0]

exudates=MyData(:,1:3)

amountExudates = MyData(:,1); %ATTRIBUTE1

total_area=MyData(:,2); %ATTRIBUTE2

densityRatio=MyData(:,3); %ATTRIBUTE3

Trainlabels=goeiedata(:,4);

k=10;

cvFolds = crossvalind('Kfold', groups, k) %# get indices of 10-fold CV

cp = classperf(groups) %# init performance tracker

for i = 1:k %# for each fold testIdx = (cvFolds == i); %# get indices of test instances

    trainIdx = ~testIdx;                     %# get indices training instances

    %# train an SVM model over training instances

    svmModel = svmtrain(exudates(trainIdx,:), groups(trainIdx), ...

                 'Autoscale',true, 'Showplot',true, 'Method','QP', ...

                 'BoxConstraint',2e-1, 'Kernel_Function','rbf', 'RBF_Sigma',1);

    %# test using test instances

    pred = svmclassify(svmModel, exudates(testIdx,:), 'Showplot',true);

    %# evaluate and update performance object

    cp = classperf(cp, pred, testIdx);
end