Prediction of Depression Prevalence Using Random Forest Regression on Global Mental Health Data

Abstrak

The primary objective of this research is to forecast the likelihood of depression by applying the Random Forest algorithm to data regarding individual characteristics. The methodology encompasses several essential phases, such as data preprocessing, feature selection, and model training, which are designed to enhance data integrity and modeling precision. Random Forest was chosen for its efficacy in managing high-dimensional datasets and its capacity to model intricate, non-linear correlations between variables. By generating multiple decision trees based on random subsets of data and features, the model effectively identifies diverse patterns associated with depression risk. Experimental outcomes indicate that the Random Forest model attains superior predictive performance, surpassing various traditional classification techniques. The model exhibits robust generalization abilities, offering dependable predictions for identifying at-risk individuals. These results imply that Random Forest is a viable and practical tool for mental health risk assessment, potentially aiding mental health professionals and policymakers in formulating early intervention strategies.