Predictive analytics for economic crisis triggered depression risk level identification among some adults in Nigeria

The striking increase in the number of depression cases globally is alarming and can be attributed to a lot of factors such as economic crisis, insecurity, high rate of terrorist attacks, and increase in the frequency of occurrence of natural disasters and epidemics. According to a recent report fro...

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Autores principales:	Bolanle A. Ojokoh, Omotolani A. Olaku, Oluwafemi A. Sarumi, Samuel I. Olotu
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Depression Predictive analytics Data mining Machine learning BDI-II Tool Economic crisis Science Q
Acceso en línea:	https://doaj.org/article/3a3d17c49c254640b1c11ae4b0ffafca
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spelling	oai:doaj.org-article:3a3d17c49c254640b1c11ae4b0ffafca2021-12-04T04:35:29ZPredictive analytics for economic crisis triggered depression risk level identification among some adults in Nigeria2468-227610.1016/j.sciaf.2021.e01056https://doaj.org/article/3a3d17c49c254640b1c11ae4b0ffafca2021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2468227621003574https://doaj.org/toc/2468-2276The striking increase in the number of depression cases globally is alarming and can be attributed to a lot of factors such as economic crisis, insecurity, high rate of terrorist attacks, and increase in the frequency of occurrence of natural disasters and epidemics. According to a recent report from the World Health Organisation, more than 322 million people are dealing with depression cases globally. In Nigeria, economic crisis and a high level of insecurity can be regarded as the major triggers for the recent surge in the number of depression cases in the country. A report in 2018 from the study conducted by the World Bank shows that 22 percent of Nigerians, on the average, are chronically depressed. In this research, we explored the use of data analytics in identifying and predicting depression risk levels among government employees in Nigeria. Specifically, we adapted the Beck Depression Inventory-II (BDI-II) psychometric tool for data collection from 500 government employees in Ondo State Nigeria and used a multiclass naíve Bayes (NB) classifier to identify the depression risk levels among the respondents. Evaluation results on discovering depression risk levels by our proposed model in our study area show accuracy and precision of 96% and 92%respectively.Bolanle A. OjokohOmotolani A. OlakuOluwafemi A. SarumiSamuel I. OlotuElsevierarticleDepressionPredictive analyticsData miningMachine learningBDI-II ToolEconomic crisisScienceQENScientific African, Vol 14, Iss , Pp e01056- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Depression Predictive analytics Data mining Machine learning BDI-II Tool Economic crisis Science Q
spellingShingle	Depression Predictive analytics Data mining Machine learning BDI-II Tool Economic crisis Science Q Bolanle A. Ojokoh Omotolani A. Olaku Oluwafemi A. Sarumi Samuel I. Olotu Predictive analytics for economic crisis triggered depression risk level identification among some adults in Nigeria
description	The striking increase in the number of depression cases globally is alarming and can be attributed to a lot of factors such as economic crisis, insecurity, high rate of terrorist attacks, and increase in the frequency of occurrence of natural disasters and epidemics. According to a recent report from the World Health Organisation, more than 322 million people are dealing with depression cases globally. In Nigeria, economic crisis and a high level of insecurity can be regarded as the major triggers for the recent surge in the number of depression cases in the country. A report in 2018 from the study conducted by the World Bank shows that 22 percent of Nigerians, on the average, are chronically depressed. In this research, we explored the use of data analytics in identifying and predicting depression risk levels among government employees in Nigeria. Specifically, we adapted the Beck Depression Inventory-II (BDI-II) psychometric tool for data collection from 500 government employees in Ondo State Nigeria and used a multiclass naíve Bayes (NB) classifier to identify the depression risk levels among the respondents. Evaluation results on discovering depression risk levels by our proposed model in our study area show accuracy and precision of 96% and 92%respectively.
format	article
author	Bolanle A. Ojokoh Omotolani A. Olaku Oluwafemi A. Sarumi Samuel I. Olotu
author_facet	Bolanle A. Ojokoh Omotolani A. Olaku Oluwafemi A. Sarumi Samuel I. Olotu
author_sort	Bolanle A. Ojokoh
title	Predictive analytics for economic crisis triggered depression risk level identification among some adults in Nigeria
title_short	Predictive analytics for economic crisis triggered depression risk level identification among some adults in Nigeria
title_full	Predictive analytics for economic crisis triggered depression risk level identification among some adults in Nigeria
title_fullStr	Predictive analytics for economic crisis triggered depression risk level identification among some adults in Nigeria
title_full_unstemmed	Predictive analytics for economic crisis triggered depression risk level identification among some adults in Nigeria
title_sort	predictive analytics for economic crisis triggered depression risk level identification among some adults in nigeria
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/3a3d17c49c254640b1c11ae4b0ffafca
work_keys_str_mv	AT bolanleaojokoh predictiveanalyticsforeconomiccrisistriggereddepressionrisklevelidentificationamongsomeadultsinnigeria AT omotolaniaolaku predictiveanalyticsforeconomiccrisistriggereddepressionrisklevelidentificationamongsomeadultsinnigeria AT oluwafemiasarumi predictiveanalyticsforeconomiccrisistriggereddepressionrisklevelidentificationamongsomeadultsinnigeria AT samueliolotu predictiveanalyticsforeconomiccrisistriggereddepressionrisklevelidentificationamongsomeadultsinnigeria
_version_	1718372905176268800

Predictive analytics for economic crisis triggered depression risk level identification among some adults in Nigeria

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