Background: The present study proceeds to incorporate feature selection as a means
for selecting the most relevant features affecting the prediction of cash prices in Iran in terms of
health economics. Health economics is an academic field that aids in ameliorating health conditions
so as to make better decisions in regard to the economy such as determining cash prices.
Methods: Accordingly, a series of search algorithms, namely the Best-First, Greedy-Stepwise, and
Ranker methods, are deployed in order to extract the most relevant features from among 500 data
samples. The validity of the methods was evaluated via the LMT procedure. The corresponding
dataset used for this study constitutes a variety of features including net cash flow, dividends,
revenue from short and long-term deposits, cash flow from investment returns, income tax, fixed
asset purchases, fixed asset sales, long-term investment purchases, long-term investment sales, total
cash flow from investment activities, financial facilities, and repayment of financial facilities.
Results: The results were indicative of the superiority of the Ranker model using the RelieFAttribute-
Eval tool in Weka over the remaining classification methods. Ergo, the LMT approach
could be employed to remove data redundancies and accelerate the estimation process, while saving
time and money. The results of the multi-layer perceptron (MLP) further confirmed the high
accuracy of the proposed method in estimating cash prices.
Conclusion: The present research attempts to reduce the volume of data required for predicting
the end cash by means of employing a feature selection method so as to save both the precious
money and time.