Cerebral injuries after cardiac arrest are serious causes for morbidity. Many previous researches in the medical society
have been proposed to prognosticate the functional recoveries of post-resuscitated patients after cardiac arrest, but the validity of suggested features and the automation of prognostication have not been made yet. This paper presents the automatic classification method which predicts the potential clinical outcomes of post-resuscitated patients who suffered from cardiac arrest. The global features and the local features are adapted from the researches from the medical society. The global features, which are consisted of the percentage of the partial volume under the uniformly increasing thresholds, represent the global tendency of apparent diffusion coefficient value in a DWI. The local features are localized and measured on the refined local apparent diffusion coefficient minimal points. The local features represent the ischemic change of small areas in a brain. The features are trained and classified by the random forest method, which have been widely used in the machine learning society for classification. The validity of features is automatically evaluated during the classification process. The proposed method achieved the 0.129 false-positive rate while maintaining the perfect true-positive rate. The area-under-curve of the proposed method was 0.9516, which showed the feasibility and the robustness of the proposed method.