Textual and numerical data fusion for depression detection: a machine learning framework - Aziz, Mohammad Tarek; Mahmud, Tanjim; Abdul Aziz, Md Faisal Bin; Siddick, Md Abu Bakar; Sharif, Md. Maskat; Hossain, Mohammad Shahadat; Andersson, Karl (Indonesian Journal of Electrical Engineering and Computer Science, 2025)
Depression, a widespread mood disorder, significantly affects global mental health. To mitigate the risk of recurrence, early detection is crucial. This study explores socioeconomic factors contributing to depression and proposes a novel machine learning (ML)-based framework for its detection. We develop a tailored questionnaire to collect textual and numerical data, followed by rigorous feature selection using methods like backward removal and Pearson’s chi-squared test. A variety of ML algorithms, including random forest (RF), support vector machine (SVM), and logistic regression (LR), are employed to create a predictive classifier. The RF model achieves the highest accuracy of 96.85%, highlighting its effectiveness in identifying depression risk factors. This research advances depression detection by integrating socioeconomic analysis with ML, offering a robust tool for enhancing predictive accuracy and enabling proactive mental health interventions.