Comparison of Machine Learning Algorithms for Predicting the | 44322

Journal of Health and Medical Research


Comparison of Machine Learning Algorithms for Predicting the Out of Pocket Medical Expenditures in Rwanda

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 of household in Rwanda is a big challenge for the patients and decision makers. It is in this regard that our paper aimed to predict the out of pocket medical expenditures in Rwanda using machine learning approaches and compares the results using four machine learning approaches such as Random Forest, Decision tree Models, Gradient Boosting, Regression tree models. The data to use for this analysis was collected from National Institute of Statistics (NISR) that is the Integrated Living Conditions Survey 2016-2018 (EICV5). However, this nationally representative survey gathered data from over 14580 households and representing 64314 individuals throughout the country. Information was collected at the household and the individual level. Household level information such as the out-of-pocket health expenditures including: consultation; laboratory tests; hospitalization; Diabetes, blood pressure, and other illness and medication costs. Decision tree Train accuracy was: 65% and Test accuracy was 67%, Random Forest Train accuracy was 77% and Test accuracy was 68% and gradient boosting was selected as the best model because the Train accuracy was 78% and Test accuracy was 85%, a variable total consumption played a significant role in the model up to 59.15%.

However, the findings proved that suggest that medical expenditures are significantly correlated with the total consumption and ages and shows that there is significant correlation in health care expenditures. Machine learning models can help to accurately forecast the expenditures. These results could advance the field toward precise preventive care to lower overall health care costs and deliver care more efficiently. Moreover, for health insurers and increasingly healthcare delivery systems, accurate forecasts of likely costs can help with general business planning in addition to prioritizing the allocation of scarce care management resources. Furthermore, for patients, knowing in advance their likely expenditures for the next year could potentially allow them to choose insurance plans with appropriate deductibles and premiums. Finally, it was found that gradient boosting increased prediction efficiency and accuracy than other machine learning used in this paper.