Cardiac patients can be regularly monitored using low cast sensor networks which can save many lives and valuable time of experts. This monitoring can be more effective if in addition to standard clinical parameters genetic information is used because of its ability to predict hereditary diseases like cardiac problems. Current clinical practices, however, only stress on physiological observation to predict heart failure rate which could miss the important information which could lead to fatal consequences. This paper presents Ambient Cardiac Expert (ACE) which combines physiological parameters observed using sensor networks with gene expression data to predict the heart failure rate. The system uses well established Support Vector Machines (SVM) for class prediction and uses Wrapper Evolutionary Algorithm based on Gaussian Estimation of Distribution Algorithm (EDA) to determine cardiac patient's criticality. Results suggest that ACE can be successfully applied for cardiac patient monitoring and has ability to integrate the information from both clinical and genetic sources.