Abstract:
In the last few years, prediction of patient’s length of stay (LOS) in a hospital is one of
the major research areas in healthcare and data mining. Here, the primary objective is
to develop a predictive model that can help to reduce the unnecessary hospitalization and
readmissions based on the historical data. Such predictive model will also help to reduce
the resources spent on unnecessary hospitalization and readmissions. In this thesis, we have
developed a predictive model that is based on the ensemble or blending of different individual
predictors. Here, performance of individual predictors and their ensemble was evaluated on
Health Heritage Prize (HHP) dataset that contains 2.6 million historical claims of 218, 415
patients. It was observed that an ensemble is significantly more effective than any individual
models used in this study.
