Textual and numerical data fusion for depression detection: a machine learning framework

Mohammad Tarek Aziz, Tanjim Mahmud, Md Faisal Bin Abdul Aziz, Md Abu Bakar Siddick, Md. Maskat Sharif, Mohammad Shahadat Hossain, Karl Andersson

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.


Keywords


Chit-squared test; COVID-19; Depression; Machine learning algorithms; Random forest

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DOI: http://doi.org/10.11591/ijeecs.v38.i2.pp1231-1244

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Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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