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在卢旺达青年中基于机器学习的心理健康预测建模

Fauste Ndikumana^1,2, Josias Izabayo³, Joseph Kalisa³

¹African Center of Excellence in Data Sciences, University of Rwanda, Kigali, Rwanda. nfauste@gmail.com.

Scientific reports

|May 9, 2025

概括

机器学习准确地预测了卢旺达年轻人的心理健康脆弱性. 创伤事件,暴力,重度饮酒和家族病史是关键的危险因素,突出了针对性干预的必要性.

科学领域:

全球心理健康全球心理健康
计算精神病学是一种计算精神病学.
公共卫生信息学公共卫生信息学

背景情况:

精神疾病对全球健康构成重大负担,不成比例地影响低收入和中等收入国家.
卢旺达的精神疾病患病率很高,特别是在1994年针对图西人的种族灭绝中幸存者中.
机器学习 (ML) 显示出在心理健康数据中识别复杂模式的潜力,但其在卢旺达的应用有限.

研究的目的:

应用机器学习技术来预测卢旺达年轻人的心理健康脆弱性.
为了确定与精神健康障碍和伴随性疾病相关的重大风险因素,在这个人群中.
探索ML在理解冲突后环境中的心理健康决定因素方面的实用性.

主要方法:

利用了来自卢旺达生物医学中心心理健康横截面研究的5221名卢旺达年轻人的数据集.
采用了四种机器学习模型:逻辑回归,支持矢量机,随机森林和梯度提升.
对预测心理健康脆弱性和精神障碍并发症的评估模型性能.

主要成果:

随机森林模型在预测心理健康脆弱性方面表现出最高准确率 (88.8%).
随机森林模型在预测精神障碍并发症方面取得了75%的准确性.
确定的重大风险因素包括暴露于创伤性事件和暴力,重度饮酒以及家族精神健康问题史.

结论:

机器学习有效地预测了与卢旺达年轻人心理健康脆弱性和并发症相关的因素.
社会因素 (创伤,暴力) 和生物学因素 (家族病史) 显著导致精神健康障碍.
卢旺达的心理健康干预和政策应该优先考虑遇到社会困难的年轻人,特别是那些遭受暴力和创伤的人.

关键词:

机器学习是机器学习.心理健康心理健康预测预测预测卢旺达卢旺达卢旺达卢旺达卢旺达卢旺达卢旺达卢旺达卢旺达年轻人年轻人年轻人年轻人年轻人

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