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Algorithms to predict shock outcome based on ventricular fibrillation (VF) waveform features are potentially useful tool to optimize defibrillation strategy (immediate defibrillation versus cardiopulmonary resuscitation). Researchers have investigated numerous predictive features and classification methods using single VF feature and/or their combinations, however reported predictabilities are not consistent. The purpose of this study was to validate whether combining VF features can enhance the prediction accuracy in comparison to single feature. The analysis was performed in 3 stages: feature extraction, preprocessing and feature selection and classification. Twenty eight predictive features were calculated on 4s episode of the pre-shock VF signal. The preprocessing included instances normalization and oversampling. Seven machine learning algorithms were employed for selecting the best …