揭露阴影:一个数据驱动的洞察力,孟加拉大学生抑郁症
Sanjib Kumar Sen1, Md Shifatul Ahsan Apurba1, Anika Priodorshinee Mrittika1,2
1Department of Computer Science and Engineering, BRAC University, Dhaka, Bangladesh.
Heliyon
|January 13, 2025
概括
这项研究使用机器学习识别了孟加拉大学生的抑郁症,发现随机森林最有效. 它提供了量身定制的建议,以支持学生在教育环境中的心理健康.
科学领域:
- 心理健康研究 心理健康研究
- 计算精神病学是一种计算精神病学.
- 教育心理学教育心理学
背景情况:
- 抑郁症是全球心理健康危机,特别影响孟加拉国等发展中国家的大学生,原因是独特的压力因素.
- 导致学生抑郁的因素包括学术压力,社会期望,财务限制,耻辱和有限的心理健康意识.
- 了解这些具体挑战对于制定有针对性的干预措施至关重要.
研究的目的:
- 在孟加拉国大学生中检测抑郁症并确定其潜在原因.
- 为患有抑郁症状的学生提供简单,量身定制的建议.
- 提高该国教育机构内的精神健康.
主要方法:
- 在与心理健康专业人员合作开发了一项调查.
- 七个机器学习模型 (SVM,K-NN,GNB,DT,RFC,ANN,GB) 被训练并测试了750名学生的数据.
- 这项研究旨在确定最准确的抑郁症预测算法.
主要成果:
- 随机森林分类器 (RFC) 在预测抑郁症方面表现优越.
- RFC实现了87%的准确度,78%的精度,95%的回忆率和86%的f1得分.
- 该模型有效地识别了抑郁症状的早期迹象.
结论:
- 机器学习,特别是随机森林,提供了一个强大的工具来检测学生群体的抑郁症.
- 通过准确的抑郁症预测,可以促进及时和有针对性的干预.
- 这项研究通过解决学生心理健康问题,有助于促进孟加拉国更健康的教育环境.
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