Feature analysis on heart failure classes and associated medications

Heart failure (HF) is a major public health problem with an increasing prevalence that has tremendous impact to patients all over the world. However, the signs and symptoms of HF in the early stages are not clear, so it is relatively difficult to prevent or predict. HF is also one of complicated diseases, there are yet no strict standards to classify various types of HF patients based on various tendencies in each cause. To discover new evidences and associated medications for each type of HF according to some known features, we could divide HF patients into four major subgroups including systolic, diastolic, valvular, and non-specified types. We have performed statistical analysis and verified prediction results by several selected features for each clustered group from CGMH patient medical records. The constructed reference models could provide necessary measurements and prognosis indications for HF patients to prevent from heart deterioration. Based on automatic medication record analysis, we applied SVM tools to train and classify the associations for different HF types. The predicted results achieved an accuracy rate of 75.26% through a 10-folds cross validation mechanism. In addition, the proposed system effectively predicted patient's survival year and life expectancy with accuracy rates between 80%–87% under different parameter settings.