  With the emergence of the Hospital Readmission Reduction Program of the
Center for Medicare and Medicaid Services on October 1, 2012, forecasting
unplanned patient readmission risk became crucial to the healthcare domain.
There are tangible works in the literature emphasizing on developing
readmission risk prediction models; However, the models are not accurate enough
to be deployed in an actual clinical setting. Our study considers patient
readmission risk as the objective for optimization and develops a useful risk
prediction model to address unplanned readmissions. Furthermore, Genetic
Algorithm and Greedy Ensemble is used to optimize the developed model
constraints.
