Ideation of Depression and Suicide Using Machine Learning Techniques

Abstract

Depression has emerged as a major mental health challenge globally, with a noticeable rise in prevalence in Bangladesh, particularly accompanied by increasing suicidal tendencies. This study investigates the underlying causes of depression and presents a machine learning-based approach for its early detection. Unemployment, family pressure, work stress, and social isolation were identified as key contributing factors. This issue was addressed using several supervised machine learning models, including Nave Bayes, Support Vector Machine (SVM), Logistic Regression (LR), Random Forest, Linear Discriminant Analysis (LDA), AdaBoost, Decision Tree, and k-Nearest Neighbor (k-NN). A comprehensive dataset related to mental health and depression symptoms was used to train and test these models. Statistical Machine Learning has shown to be the most accurate and consistent method while Logistic Regression provided the most consistent and balanced performance. Naïve Bayes had good recall capabilities, and AdaBoost had robust performance across a variety of metrics. Additionally, Random Forest and k-NN provided reliable results, while Decision Tree and LDA did not produce any interpretable yet effective results. This study confirms the potential of machine learning techniques for the accurate detection of depression and related mental health issues. It will be important to enhance model explanation, reduce algorithmic bias, integrate diverse data sources, and adhere to ethical principles like privacy protection and informed consent for future research. In resource-constrained regions like Bangladesh, AI-driven mental health tools are especially important for enabling timely diagnosis and support.