Mortality prediction is a determinant factor in Intensive Care Units (ICU). The high costs associated with a patient’s stay in an ICU can place a burden on any health care system around the world. The ability to accurately predict the risks of patients dying can provide important insight to help hospitals manage resources efficiently and therefore reduce costs and help reduce the number of fatalities caused by CHF. Another crucial factor is the data availability which has become more prominent with the usage of electronic health records (EHR) by the majority of hospitals. This makes it possible to extract patient’s health records during their stay in an ICU, allowing models to use that information to make precise predictions. In this analysis, two versions of the same dataset are used. One version contains measurements of various chart variables while the other version contains measurements of chart and lab variables. Long short-term memory (LSTM) and Gated Recurrent Unit (GRU) were used to predict if a patient will die after a gap window of at least 4 hours, 8 hours and 12 hours after the last measurement, where the gap window is a time interval that separates the time of the last measurement used for making a prediction and the time of the actual event (death in hospital or discharge from hospital). For all classifiers, as the gap window increases, the model performance decreases as well. Moreover, it is clearly noticeable that the performance increases as the lab data is included into the training process of the predictive model. The highest AUC score obtained was 88.53% using LSTM with a 4-hour prediction window on chart and lab variables. The smallest AUC score was approximately 72.07% using LSTM with a 12-hour prediction window on chart variables only. Sensitivity and specificity obtained for each model and prediction windows is also described in detail in this thesis.