Forecasting Global Mental Health Disorders: A Machine Learning Approach using Socioeconomic Indicators

Puradkar, Sonal Suryakant (2023) Forecasting Global Mental Health Disorders: A Machine Learning Approach using Socioeconomic Indicators. Masters thesis, Dublin, National College of Ireland.

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Abstract

This research examines the global impact of socioeconomic indicators on the prevalence of mental health disorders, with a focus on depressive disorders, schizophrenia, bipolar disorder, eating disorders, and anxiety disorders. Employing machine learning techniques and data from Our World in Data and the World Bank spanning 1960-2019, we predict and analyze the influence of Adjusted Net National Income per capita, Inflation, Employment distribution, Proportion of people below median income, Unemployment rates, New businesses registered, and Multidimensional Poverty. The study aims to identify correlations, forecast disorder trends, and pinpoint countries with heightened vulnerability. Insights gained from this research will inform targeted interventions, promoting a proactive approach to global mental health challenges.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Chikkankod, Arjun UNSPECIFIED
Subjects:	H Social Sciences > HT Communities. Classes. Races Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning R Medicine > RA Public aspects of medicine > RA790 Mental Health
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	20 May 2025 16:23
Last Modified:	20 May 2025 16:23
URI:	https://norma.ncirl.ie/id/eprint/7594

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