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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Abdul Aziz, Md Faisal Bin
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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 Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp1231-1244

Abstract

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.
Co-Authors Andersson, Karl Aziz, Mohammad Tarek Mahmud, Tanjim Mohammad Shahadat Hossain, Mohammad Shahadat Sharif, Md. Maskat Siddick, Md Abu Bakar