Predictive Analytics in Mental Health: A Machine Learning Approach to Assessing Depression Severity

Abstract

Contemporary healthcare reforms are being significantly shaped by the ongoing advance-ments in technology. One area where these advancements are proving crucial is in the un-derstanding and treatment of depression, which is increasingly emerging as a substantial public health concern. To address this issue, there is a growing interest in leveraging novel research methods and therapeutic approaches to identify the contributing factors to de-pression. This study adopts an innovative approach, utilizing machine learning techniques to carry out an exhaustive examination of diverse data sources. The primary aim is to gain a profound comprehension of the complex interplay between various facets of individuals' quality of life and the presence of depression. To undertake this investigation, the research-ers have harnessed the National Health and Nutrition Examination Survey (NHANES) Da-taset provided by the Centers for Disease Control and Prevention, a rich source of valuable health-related information.
In this study, the focus is on exploring the behavioral and social dimensions of numerous subjects and their intricate connections to depression. To achieve this, a diverse array of machine learning classifiers has been deployed, including the Decision Tree Classifier, Ran-dom Forest Classifier, Gaussian Naive Bayes, KNN Classifier, Logistic Regression, Support Vector Machine Classifier, and Multilayer Perceptrons. By applying these classifiers, the re-searchers aim to assess their performance across various metrics, providing valuable insights into which models are best suited to discern the connection between depression and as-pects of quality of life.