Cardiovascular disease (CVD) is one of the major causes of death all over the world and the mortality rate is higher than other causes. Hence, we propose a novel deep neural network (DNN)-based prediction model for the major adverse cardiovascular event (MACE) occurrences in patients with non-ST-Elevation myocardial infarction (NSTEMI) to improve the prediction accuracy of CVD. The research contents are described as follows. First, for the experiment, we use the Korean Acute Myocardial Infarction Registry (KAMIR-NIH) dataset with 2 years follow-ups and then preprocess the extracted data, such as processing the missing values, solving the imbalance problem, and applying the normalization meth to scale all the datasets in the same range for the experiment. Then we design a DNN-based prognosis model for the occurrences of MACE in NSTEMI patients. Finally, we evaluate the proposed model’s performance and compare it with several applied machine learning algorithms, such as logistic regression, K-Nearest Neighbors, decision tree, and support vector machine. The result shows that the performance of our proposed method outperformed other machine learning-based prediction models.