The Use of Feature Selection Algorithm and Regression Method to Predict the Ending Cash Value in Health Economics

Author(s): Zinat Ansari*

Journal Name: Current Signal Transduction Therapy

Volume 16 , Issue 1 , 2021


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Abstract:

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 from 2010-2012.

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.

Keywords: Ending cash, feature selection, MLR (Multiple linear regression), payment, algorithm, health economics.

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Article Details

VOLUME: 16
ISSUE: 1
Year: 2021
Published on: 22 October, 2019
Page: [13 - 22]
Pages: 10
DOI: 10.2174/1574362414666191022162244

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