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189427

A comparative study between Logistic Regression and Neural Networks for examining factors influencing child mortality in Libya.

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Last updated: 05 Jan 2025

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Abstract

Child mortality remains one of the most critical issues under investigation. This study focuses on describing the phenomenon of child mortality in Libya and examining the factors influencing it. Since child mortality is a variable that follows a Bernoulli process, logistic regression is utilized to estimate the model that represents the relationship between the variables and employs it for statistical prediction. Logistic models also enable prediction of the occurrence or non-occurrence of specific events. However, certain assumptions may not hold true, prompting the use of neural networks. Neural networks have the capability to model data and make predictions without relying on specific assumptions about the nature of the variables or their relationships.

DOI

10.21608/esju.2020.189427

Keywords

Neural Networks, Logistic regression, Infant Mortality, Libya

Volume

64

Article Issue

2

Related Issue

26947

Issue Date

2020-12-01

Receive Date

2021-08-15

Publish Date

2020-12-01

Page Start

1

Page End

26

Print ISSN

0542-1748

Online ISSN

2786-0086

Link

https://esju.journals.ekb.eg/article_189427.html

Detail API

https://esju.journals.ekb.eg/service?article_code=189427

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1

Type

Original Article

Type Code

1,914

Publication Type

Journal

Publication Title

The Egyptian Statistical Journal

Publication Link

https://esju.journals.ekb.eg/

MainTitle

A comparative study between Logistic Regression and Neural Networks for examining factors influencing child mortality in Libya.

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Type

Article

Created At

23 Jan 2023