A Neural Computing-based Cash Price Prediction Using Multi-layer Perceptron (MLP) and Feature Selection for Health Economics

(E-pub Ahead of Print)

Author(s): Zinat Ansari*

Journal Name: Current Signal Transduction Therapy

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Abstract:

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 are between academic fields that can aid in ameliorating conditions so as to perform better decisions in regards 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 a 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 RelieF-Attribute-Eval tool in Weka over the remaining classification methods. Ergo, the LMT approach could be employed to remove data redundancies and thereby 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.

Conclusions: 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: Feature Selection Algorithm, Cash Price, Multi-Layer Perceptron (MLP)

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Article Details

(E-pub Ahead of Print)
DOI: 10.2174/1574362414666191127111916

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