Management of healthcare’s resources contributes to improving the quality of
medical services, thereby enhancing the level of health of society in general. This
management requires providing prospective information about the need for patients
admitting in a hospital, and the necessary medical resources.Prediction techniques represent
an effective tool for knowledge discovery in huge and complex datasets in many fields
including healthcare.
Methods: In this work, we design and implement a prediction model called a
Modern Prediction Model for HealthCare Problem (MPM-HCP) which introduces two
improvements for Gradient Boosting Machine (GBM) prediction technique. MPM-HCP
developed (GBM) by inspiring positive sides of linear regression to replace splitting
criterion with a correlation measure in regression tree building. It also reduced the
complexity of building boosted model by using a fast method for choosing best split point.
Results: MPM-HCP has significant behavior in terms of prediction error and
execution time. In comparison with tradition gradient boosting trees, the MPM-HCP has a
testing error of 0.468,while original GBM based on sum of squared has error of0.491, and
original GBM based on standard deviations has 0.481 error. Training time is also reduced
more than 85%.
Conclusions: MPM-HCP implementation showed that there were three attributes
frequent in binary regression trees building. Those attributes were gender of patient,
number of claims to admit hospital, and the medical procedure group, which means those
attributes is more correlated with the target of prediction (i.e. number of hospitalization
days). MPM-HCP confirms the ability to produce precise prediction result, and the
scalability to deal with huge dataset in suitable execution time.