Sen Sanjib Kumar, Apurba Md Shifatul Ahsan, Mrittika Anika Priodorshinee, Anwar Md Tawhid, Al Islam A B M Alim, Noor Jannatun
Department of Computer Science and Engineering, BRAC University, Dhaka, Bangladesh.
Computing for Sustainability and Social Good (C2SG) Research Group, Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh.
Heliyon. 2024 Dec 10;11(1):e41110. doi: 10.1016/j.heliyon.2024.e41110. eCollection 2025 Jan 15.
Depression is more than just feeling sad. It is a severe and multifaceted mental health condition that impacts millions of individuals around the globe. Regrettably, it can even be more prevalent in university students of underdeveloped and developing countries like Bangladesh because of academic pressure, family and societal expectations, financial limitations, stigmatized social and cultural norms, unemployment concerns, lack of mental health awareness, etc. Each of these factors can play a significant role in leading someone towards depression, with their impact varying from person to person. This research, along with detecting depression and gaining insights into the reasons behind the prevalence of depression in this specific population, also focuses on providing simple yet important and tailored recommendations to those who need them. To achieve these objectives, a survey was meticulously designed in collaboration with psychologists, counselors, and therapists. Seven machine learning models, including Support Virtual Machine (SVM), K-Nearest Neighbor (K-NN), Gaussian Naive Bayes (GNB), Decision Tree (DT), Random Forest Classifier (RFC), Artificial Neural Network (ANN), and Gradient Boosting (GB), were trained and tested using the collected data (n = 750) to identify the most effective method for predicting depression. After rigorous analysis, Random Forest emerged as the best-performing algorithm, exhibiting remarkable accuracy (87%), precision (78%), recall (95%), and f1-score (86%). This research mainly strives to identify the initial signs of depressive symptoms among Bangladeshi university-going students and facilitate timely and targeted interventions for the affected individuals. By doing so, it ultimately aims to contribute to building a brighter, healthier, and more resilient educational environment in the country.
抑郁症不仅仅是感到悲伤。它是一种严重且多方面的心理健康状况，影响着全球数百万人。遗憾的是，由于学业压力、家庭和社会期望、经济限制、受污名化的社会和文化规范、就业担忧、心理健康意识缺乏等因素，在孟加拉国等不发达国家和发展中国家的大学生中，抑郁症可能更为普遍。这些因素中的每一个都可能在导致某人患上抑郁症方面发挥重要作用，其影响因人而异。这项研究除了检测抑郁症并深入了解这一特定人群中抑郁症流行的原因外，还专注于为有需要的人提供简单但重要且量身定制的建议。为实现这些目标，与心理学家、顾问和治疗师合作精心设计了一项调查。使用收集到的数据（n = 750）对包括支持向量机（SVM）、K近邻（K-NN）、高斯朴素贝叶斯（GNB）、决策树（DT）、随机森林分类器（RFC）、人工神经网络（ANN）和梯度提升（GB）在内的七种机器学习模型进行了训练和测试，以确定预测抑郁症的最有效方法。经过严格分析，随机森林成为表现最佳的算法，具有显著的准确率（87%）、精确率（78%）、召回率（95%）和F1分数（86%）。这项研究主要致力于识别孟加拉国大学生中抑郁症状的初始迹象，并为受影响的个体提供及时且有针对性的干预措施。通过这样做，它最终旨在为该国建设一个更光明、更健康、更具韧性的教育环境做出贡献。
