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Accurately predicting the length of stay of patients in hospital can have positive impacts on both financial and psychological conditions of patients. This can also help hospital administrations to perform effective and correct allocation of critical resources to patients. This work presents an ensemble modeling technique that can be used for accurate determination of length of stay of patients based on several factors that are obtained from hospital records. The multi model nature of the architecture, and the heterogeneity associated with the production model provides a high performing system that can handle the complex hospital data. Experiments were performed on the standard MIMIC III data. Comparisons were performed with the existing state of the art models from literature. Comparisons indicate that the proposed HMME model demonstrations accuracy levels of 95%, indicating that the model can be effectively deployed in hospitals for decision making purposes. Keywords: Length of state prediction; ensemble modeling; multi model architecture; MIMIC III; correlation based analysis

Managing hospital resources is one of the major requirements in providing qualitative and timely treatments do patients. From the patient’s standpoint, identifying the number of days of stay can aid in effective planning from the monetary perspective.This work presents the Feature Augmented Stacked Bagging (FASB) model for effective prediction of length of stay of patient based on their hospital records. The ensemble model created is composed of two levels; the first level operates on the hospital data and provides the intermediate predictions, while the second level uses predictions from the first level model to perform the final predictions. Experiments where performed using the MIMIC III data, and the results obtained were compared with existing state of the art models for analysis. Comparisons indicate that the proposed model exhibits 95% accuracy levels, which is 13% greater than the compared model. This indicates that the proposed model is highly effective in identifying the length of stay of patients.