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

According to the World Health Organization, over 300 million people worldwide are affected by major depressive disorder (MDD) [1]. 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 [2]. 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) [3]. 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.