在未来的大学生中预测抑郁症和焦虑症状的预测建模:基于GIS和机器学习的研究
Mohammed A Mamun1,2,3, Firoj Al-Mamun1,2,3, Md Emran Hasan1,4
1CHINTA Research Bangladesh, Dhaka, Bangladesh.
Psychological reports
|July 14, 2025
概括
近30%的未来大学生患有抑郁和焦虑. 关键的风险因素包括女性性别,城市生活和过去的自杀念头,需要有针对性的心理健康支持.
科学领域:
- 公共卫生 公共卫生
- 心理健康研究 心理健康研究
- 在医疗保健中的数据科学.
背景情况:
- 未来的大学生面临着重大的心理健康挑战,包括抑郁和焦虑.
- 伴随性抑郁症和焦虑症是普遍存在的问题,需要对其风险因素和分布进行调查.
研究的目的:
- 在未来的大学生中调查伴随性抑郁症和焦虑症的患病率和危险因素.
- 使用地理信息系统 (GIS) 和机器学习 (ML) 分析空间分布和预测因素.
主要方法:
- 在对1485名参与者的横截面调查中,收集了社会人口统计,教育,心理和地理数据.
- 使用SPSS的统计分析,使用Python的ML (XGBoost,GBM,CatBoost),以及使用R的GIS分析 (4.3.1版本",孟加拉国"包).
主要成果:
- 29.0%的参与者报告了伴随性抑郁和焦虑.
- 确定了重要的风险因素:女性性别,城市居住,联合家庭,商业背景,自我指导,模拟测试不满,更高的支出,过去的自杀念头和先前的心理健康问题.
- XGBoost SHAP发现过去的自杀念头是最强的预测因素;GIS绘图显示了区域差异,特别是在奇塔贡山区.
结论:
- 针对性干预和支持性政策对于解决大学入学者的心理健康问题至关重要.
- 教育机构,医疗保健提供者,政策制定者和社区之间的协作努力对于有效的心理健康支持至关重要.
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