通过机器学习技术预测自杀结果的影响因素:来自伊朗西部自杀登记计划的见解
Nasrin Matinnia1, Behnaz Alafchi2, Arya Haddadi3
1Nursing Department, Faculty of Medical Sciences, Hamedan Branch, Islamic Azad University, Hamedan, Islamic Republic of Iran.
机器学习模型预测了伊朗哈马丹的自杀风险因素. 随机森林模型的表现优于其他模型,确定自杀方法,年龄,收入和动机是有针对性的预防的关键决定因素.
科学领域:
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
- 精神病学是一个精神病学.
背景情况:
- 全球和伊朗哈马丹的自杀率正在上升.
- 了解区域性自杀决定因素对于公共卫生至关重要.
- 现有数据需要先进的分析方法来进行预测.
研究的目的:
- 使用机器学习预测影响自杀结果的因素.
- 为了比较天真贝叶斯,随机森林和后勤回归模型的有效性.
- 确定哈马丹省自杀的关键决定因素.
主要方法:
- 利用了哈马丹自杀登记计划 (2016-2017年) 的数据.
- 应用了天真贝叶斯,随机森林和后勤回归算法.
- 进行了变量重要性和多重物流回归分析.
主要成果:
- 随机森林模型展示了卓越的预测性能.
- 确定了关键决定因素:自杀方法,年龄,尝试的时间,收入和动机.
- 文化背景显著影响自杀方法,特别是在老年人身上.
结论:
- 机器学习模型可以有效地预测自杀风险因素.
- 预防计划必须根据特定的决定因素和文化背景量身定制.
- 需要进一步的研究来完善哈马丹的预测模型和预防策略.
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