Abstract
In-Hospital Cardiac Arrest (IHCA) has been an important issue in hospitals, and the IHCA patients in Taiwan have much lower survival and discharge rate than in the United States. The effectiveness of resuscitation may ultimately determine whether a patient can survive or discharge from the hospital. The data mining methods have rarely been applied to IHCA cases and to extract useful rules to assist medical personnel in decision making. Therefore, it is desired to study the application of support vector machines (SVM) in integrating a set of methods to provide a better understanding of the relationship between IHCA and discharge rate for the hospital personnel. To solve the problem of insufficient explanatory power of SVM, this study integrates three rule extraction algorithms, the C5.0 decision trees, classification and regression trees (CART), and rough sets, with the support vectors generated from SVM to extract rules and enhance SVM's explanatory power. Five indexes including accuracy, sensitivity, specificity, fidelity, and coverage are used to evaluate the classification performance of the three proposed methods on the UCI database and IHCA case. The results show that all three proposed methods can obtain more than 80% overall accuracy of classification. Thus, the proposed methods not only can be applied successfully on the UCI dataset, but also can provide useful reference for medical decisions in the real case of IHCA.