Department of Applied Statistics, School of Economics, University of Rwanda, Kigali, Rwanda
Research
Comparison of Machine Learning Algorithms for Predicting the Out of Pocket Medical Expenditures in Rwanda
Author(s): Roger Muremyi*, Niragire Francois, Kabano Ignace, Nzabanita Joseph and Dominique Haughton
In Rwanda, the government has done a lot for its population to access the health services easily. However, it is one of the
African countries with the high rate of people with health insurance through Community health service 96% of the
population and overall health insurance possession is around 74%. Despite all efforts and high rate of health coverage in
general there exist some gaps caused by an increase of out of pocket medical expenditures which might lead to delays of
accessing medical health care. However, one of the ways of handling this issue is to predict the out of pocket medical
expenditures with accuracy.
Moreover, machine learning algorithm have not been sufficiently used previously to predict the future health care cost in
Rwanda by considering zero health cost, thus the lack of the efficient method to be used to predict future health care cost.. View More»
