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Determining the Impact of ICT Devices on our Environment and Health using Machine Learning Techniques

by Ugochi Adaku Okengwu, Stanley Ziweritin
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International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 12 - Number 39
Year of Publication: 2022
Authors: Ugochi Adaku Okengwu, Stanley Ziweritin
10.5120/ijais2022451927

Ugochi Adaku Okengwu, Stanley Ziweritin . Determining the Impact of ICT Devices on our Environment and Health using Machine Learning Techniques. International Journal of Applied Information Systems. 12, 39 ( April 2022), 25-31. DOI=10.5120/ijais2022451927

@article{ 10.5120/ijais2022451927,
author = { Ugochi Adaku Okengwu, Stanley Ziweritin },
title = { Determining the Impact of ICT Devices on our Environment and Health using Machine Learning Techniques },
journal = { International Journal of Applied Information Systems },
issue_date = { April 2022 },
volume = { 12 },
number = { 39 },
month = { April },
year = { 2022 },
issn = { 2249-0868 },
pages = { 25-31 },
numpages = {9},
url = { https://www.ijais.org/archives/volume12/number39/1127-2022451927/ },
doi = { 10.5120/ijais2022451927 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T19:11:28.871504+05:30
%A Ugochi Adaku Okengwu
%A Stanley Ziweritin
%T Determining the Impact of ICT Devices on our Environment and Health using Machine Learning Techniques
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 12
%N 39
%P 25-31
%D 2022
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper explores the known but silent effects of ICT devices on our environment and health. The use of ICT devices is very crucial and paramount to our daily activities but the negative and positive effects especially with the new idea of Internet Of things (IOT) that will make the sensors part of our daily routine till we go to bed. The negative effect ranges from exposure to toxic compounds, high energy consumption due to high reliance on ICT devices, exposures us to non-thermal radio frequency radiation from Wifi, cellular and many more. The positive effect of ICT and its associated devices are too numerous to list which have been adopted in every spheres of human endeavour. The aim is to build a K-nearest neighbor and random forest technique to access the impact of ICT devices in detecting human heart diseases caused by ICT radiations. This will help reduce the stress of searching and waiting with hope for specialists to look at results of images when diagnosis are performed by lab scientists in determining whether the patient is fine or have heart disease. This contributes positively to the healthcare delivery system and promotes our next level of digital economy in the society at large because of the limited number of medical doctors. We adopted the k-fold cross validation test to have a better classification report. The KNN produced 90% cross validation test accuracy which was observed to be higher than the random forest with 85.71% cross validated accuracy.

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Index Terms

Computer Science
Information Sciences

Keywords

ICT devices health Internet of Things(IOT) KNN Decision tree

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