Continuous increase in fall incidents for the elderly has resulted in significant medical expenditure. As a result, there is a need for fall prevention. Fall detection system can be used to detect when a person is about to get out of bed such that early assistance can be provided. In this study, Long short-term memory neural network, a recurrent neural network capable of handling time series data, was proposed for predicting a pre-bed exit event using a simple four bed pressure sensor system. Still, pre-exit and exit states were classified based on the value from four pressure sensors, one placed under each leg of the bed. Sliding windowof 3, 5 and 10 seconds was used in assessing model performance. Different hyperparameter settings were applied in model tuning. A baseline mode, random forest, was used for comparison. To evaluate the performance, confusion matrix, accuracy, precision and recall were used. Overall accuracy of 95% was achieved when testing with near term data using LSTM model. Accuracy of random forest classifier was 90%. However, the accuracy of both models dropped when further away dates were used. The models were close in accuracy but made different mistakes.