Prediction of Mental Illness Associated with Unemployment Using Data Mining | Zendy

Carina Gonçalves | Zendy; Diana Ferreira | Zendy; Cristiaeto | Zendy; António Abelha | Zendy; José Machado | Zendy

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Prediction of Mental Illness Associated with Unemployment Using Data Mining

Author(s) -

Carina Gonçalves,

Diana Ferreira,

Cristiaeto,

António Abelha,

José Machado

Publication year - 2020

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2020.10.078

Subject(s) - computer science , mental illness , unemployment , process (computing) , software , association rule learning , data mining , data science , psychiatry , mental health , medicine , economics , programming language , economic growth , operating system

Mental illness is a concern these days, affecting people worldwide and across all kinds of ages. This article aims to predict mental illness and discover its association with unemployment as well as other possible causes behind the illness. In order to accomplish this goal, a Data Mining (DM) process was performed using the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology and the RapidMiner Studio software. In the end, the results obtained were considered promising since all the evaluation metrics, namely accuracy, sensitivity, and specificity, obtained values above 90%. The study also allowed, in the end, to identify the factors associated with the prediction of mental illness.

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