在未来的大学生中探索自杀念头:一项应用机器学习和GIS技术的研究
Mohammed A Mamun1,2,3, Firoj Al-Mamun4,5,6, Md Emran Hasan6,7
1Department of Public Health and Informatics, Jahangirnagar University, Dhaka, Bangladesh. mamun@thechinta.org.
BMC psychiatry
|August 1, 2025
概括
自杀行为在大学生中很普遍,受到人口统计学,学术和家庭史的影响. 机器学习,特别是CatBoost,有效预测自杀倾向,帮助有针对性的干预措施.
科学领域:
- 精神病学是一个精神病学.
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
背景情况:
- 大学学生面临严重的心理困扰,包括自杀行为.
- 像地理信息系统 (GIS) 地图和机器学习 (ML) 等创新方法在自杀风险评估中未得到充分利用.
- 了解空间分布和自杀倾向的预测因素对于这个弱势群体至关重要.
研究的目的:
- 调查未来大学生中自杀行为的流行率和风险因素.
- 使用GIS绘图探索自杀倾向的空间分布.
- 开发和评估用于预测自杀想法的ML模型.
主要方法:
- 从1485名参与者收集了关于社会人口统计,入学,健康和家庭因素的数据.
- 采用后勤回归来识别重大风险因素.
- 利用机器学习算法 (CatBoost,K-Nearest Neighbors) 进行预测建模.
主要成果:
- 观察到自杀念头的发病率为20.5%,女性,农村居民和联合家庭的发病率更高.
- 风险因素包括重复考试,学术上的扎,缺乏专业指导,物质使用,以及家庭精神健康问题/自杀史.
- 图形信息系统绘制突出了区域差异;CatBoost显示了自杀念头的优异预测性能,抑郁状态是最有影响的因素.
结论:
- 大学学生的自杀行为是复杂的和多因素的.
- 针对性干预和支持服务对于预防自杀至关重要.
- 像ML和GIS这样的先进分析技术为风险评估和预防策略提供了有希望的途径.
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