reinpmomz/Predicting-Depression-using-Supervised-Machine-Learning-NIDS-South_Africa

Predicting Depression in South Africa using Supervised Machine Learning # Predicting-Depression-using-Supervised-Machine-Learning-NIDS-South_Africa Predicting Depression in South Africa using Supervised Machine Learning ## Background Mental health problems, particularly depression, are a growing public health concern worldwide, and middle-income countries such as South Africa are disproportionately affected, causing a significant number of depressed persons to remain unidentified and untreated. Existing studies largely use traditional analytical methods, which are often limited by their inability to capture complex, non-linear relationships between risk factors and depression. This study utilizes an advanced analytical method by applying machine learning (ML) to provide accurate detection of depression. ## Setup We are assuming you have `R Software` and `RStudio IDE` installed. If not, you can download and install **R software**, followed by **RStudio/Posit IDE**. ## Data The data used for analysis is available from the **Data First Repository** under the **National Income Dynamics Study (NIDS)** collection via identifiers doi.org, doi.org, doi.org, doi.org, doi.org. One needs to register and request permission to utilize the publicly available datasets in the Repository. After permission is granted, download the NIDS data for the five waves in form of Stata DTA files and save them in a local folder. Each wave has its individual folder that contains 9 Stata DTA files for waves 2 - 5 (Admin_W*_Anon_V*.dta, Adult_W*_Anon_V*.dta, Child_W*_Anon_V*.dta, hhderived_W*_Anon_V*.dta, HHQuestionnaire_W*_Anon_V*.dta, HouseholdRoster_W*_Anon_V*.dta, indderived_W*_Anon_V*.dta, Proxy_W*_Anon_V*.dta, Link_File_W*_Anon_V*.dta) and 8 Stata DTA files for wave 1 (Admin_W1_Anon_V*.dta, Adult_W1_Anon_V*.dta, Child_W1_Anon_V*.dta, hhderived_W1_Anon_V*.dta, HHQuestionnaire_W1_Anon_V*.dta, HouseholdRoster_W1_Anon_V*.dta, …