Supervised Machine Learning Modelling of Demand for Outpatient Health-Care Services in Kenya using Artificial Neural Networks and Regression Decision Trees.

Machine learning models are frequently gaining wide applications in different fields with an emphasis on supervised machine learning for data exploration as in healthcare provision. With an increase in demand for outpatient health care services in Kenya, the Government is faced with the problem of forecasting this type of demand. Its in this regard that this research develops a supervised machine learning model for the modeling and prediction of demand for outpatient health-care services in Kenya using artificial neural networks, Linear Regression analysis and Decision trees. The objectives of this research were to analyze, develop and evaluate machine learning models in the modeling of outpatient healthcare data. Model evaluation was via the deviance residuals and mean squared error. Data used in this research was secondary data obtained from the Kenya Household Health Expenditure Utilization Survey, 2017 in which the R statistical software was used to aid the analysis. A total of nine thousand one hundred and fifty-nine outpatients were used in the research and data attributes were age, gender, cost of outpatient health care sought and number of visits made by the outpatients. Experiment results showed that age and gender of an outpatient were significant factors in the estimation and forecasting of demand for outpatient healthcare services for which the neural network architecture was used in the data training. Cost and Visits were used as primary variables in decision tree construction for the classification and regression decision trees respectively.