Predicting The Occurrence Of Adverse Events Using An Adaptive Neuro-Fuzzy Inference System (Anfis) Approach With The Help Of Anfis Input Selection

Keywords

Adaptive neuro-fuzzy inference systems (ANFIS); Adverse events; Economical infrastructure development; Multiple linear regression (MLR)

Abstract

This study presents an adaptive neuro-fuzzy inference system (ANFIS) approach performed to estimate the number of adverse events where the dependent variables are adverse events leading to four types of variables: number of people killed, wounded, hijacked and total number of adverse events. Fourteen infrastructure development projects were selected based on allocated budgets values at different time periods, population density, and previous month adverse event numbers selected as independent variables. Firstly, number of independent variables was reduced by using ANFIS input selection approach. Then, several ANFIS models were performed and investigated for Afghanistan and the whole country divided into seven regions for analysis purposes. Performances of models were assessed and compared based on the mean absolute errors. The difference between observed and estimated value was also calculated within ± 1 range with values around 90 %. We included multiple linear regression (MLR) model results to assess the predictive power of the ANFIS approach, in comparison to a traditional statistical approach. When the model accuracy was calculated according to the performance metrics, ANFIS showed greater predictive accuracy than MLR analysis, as indicated by experimental results. As a result of this study, we conclude that ANFIS is able to estimate the occurrence of adverse events according to economical infrastructure development project data.

Publication Date

8-1-2017

Publication Title

Artificial Intelligence Review

Volume

48

Issue

2

Number of Pages

139-155

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/s10462-016-9497-3

Socpus ID

84978043830 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84978043830

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