We propose a Bootstrap resampling approach for Feature Selection (FS) using the weights obtained by a linear Support Vector Machine (SVM) when it is applied to high-dimensional input spaces. We build our approach on a practical application with an extremely high-dimensional input space. The application is the detection of Anastomosis Leakage (AL) after colorectal cancer surgery using free text Bag-of-Words in Electronic Health Records (EHRs). Colorectal cancer is the third most common cancer type, and surgery is the only curative treatment, making the detection of AL of prime importance. The reduced input space obtained by the proposed FS strategy in combination with the linear SVM provided a much improved performance for early detection AL after colorectal cancer (earlier/final sensitivity 97%/100% and specificity 47%/89%). Further extensions of the method can be the basis for a principled FS strategy in high-dimensional input spaces. © 2014 IEEE.