A Machine-Learning Approach for Predicting Depression Through Demographic and Socioeconomic Features

Abstract

According to the World Health Organization, over 300 million people worldwide are affected by major depressive disorder (MDD). Individuals battling this mental condition may exhibit symptoms including anxiety, fatigue, and self-harm, all of which severely affect well-being and quality of life. Current trends in social media and population behavior bring up an urgent need for health professionals to strengthen mental health resources, improve access and accurately diagnose depression. To mitigate the disparate impact of depression on people of different social and racial groups, this study identifies factors that strongly correlate with the prevalence of depression in U.S. adults using health data from the 2019 pre-pandemic National Health Institute Survey (NHIS). In this study we trained a random forest model capable of performing a classification task on American-adults survey data with an accuracy of 98.7%. Our results conclude that age, education, income, and household demographics are the primary factors impacting mental health. Awareness of these mental health stressors may motivate medical professionals, institutions, and governments to identify more effectively the at-risk people and alleviate their potential suffering from MDD. By receiving adequate mental health services, Americans can improve their quality of life and form a more fulfilling society.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This study was funded by the Academy for Advanced Research and Development (AARD).

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.

Yes

Data Availability

All data produced in the present study are available upon reasonable request to the authors

留言 (0)

沒有登入
gif