Background: Health economics is amongst the academic fields which can aid in ameliorating
conditions so as to make better decisions related to the economy such as determining
cash prices. The prediction of ending cash value is fundamental for internal and external users and
can come quite handy in health economics. The most important purpose of financial reporting is
the presentation of information to predict ending cash value. Therefore, the aim of this research is
to predict ending cash value using feature selection and multiple linear regression (MLR) method
Methods: Feature selection algorithm (Best-First, Greedy-Stepwise, and Ranker) was employed in
this research to nominate relevant data that affect ending cash value.
Results: According to results, to determine ending cash value, the most relevant features include:
interest payments for loans, dividends received from short and long term deposits, total net flow of
investment activities, net increase (decrease) in cash and beginning cash based on best-first (CFSSubset-
Evaluation) and Greedy-Stepwise (CFS-Subset-Evaluation). Net out flow, dividends, dividends
paid, interest payments for loans and dividends received deposits for short and long term
were the most important data as indicated by the Ranker (Info-Gain-Attribute-Evaluation, Gain-
Ratio-Attribute-Evaluation and Symmetricer-Attribute-Evaluation). According to Ranker (Principal-
Components and Relief-FAttribute-Evaluation), the best data for determining ending cash include
beginning cash, interest payments for loans, dividends, net increase (decrease) in cash and
dividends received from short and long term deposits. The findings were also indicative of a positive
and highly significant correlation between dividends received from short and long term
deposits and beginning cash (1.00**), with a significance level of 0.01, whereas the observed correlation
between interest payments for loans and ending cash (0.999**), at a significance level of
0.01 was negatively significant.
Conclusion: The present research attempted to reduce the volume of data required for predicting
end cash by means of employing a feature selection so as to save both precious money and time.