在使用结构方程模型的研究生中,月经异常的患病率和决定因素
K Varsha1, Satyanarayana2, R Prajna1
1Department of Statistics, Mangalore University, Mangalagangotri, Konaje, Karnataka, India.
Journal of education and health promotion
|October 16, 2025
概括
高频率的月经异常影响印度研究生. 减少压力和促进健康习惯是管理这些情况和改善学生福祉的关键.
科学领域:
- 生殖健康 生殖健康
- 公共卫生 公共卫生
- 医学统计 医学统计
背景情况:
- 月经异常影响印度85-93.4%的女性,其中许多在20-29岁,包括研究生.
- 识别和管理月经异常的原因对于这一群体至关重要.
研究的目的:
- 调查研究生月经异常的患病率和危险因素.
- 使用结构方程建模 (SEM) 和机器学习开发月经障碍的预测模型.
主要方法:
- 在印度曼加罗尔大学的272名研究生女学生中进行了分层调查.
- 收集的数据包括月经史,生活方式,健康,压力和社会经济因素.
- 结构方程建模 (SEM) 和机器学习模型用于分析风险因素和预测疾病.
主要成果:
- 78%的研究生报告了月经异常,其中异常最为普遍 (24.26%).
- SEM将生活方式,压力,身体健康和实践确定为重大风险因素.
- 基于SEM的分类模型在预测月经障碍方面取得了84.56%的准确性.
结论:
- 减少压力和促进健康的生活方式对于管理月经异常至关重要.
- 有针对性的干预措施可以增强月经健康,积极影响学业成绩和整体幸福感.
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