季节性情绪障碍检测的新框架:使用多式联络社交媒体数据和SMOTE进行全面的机器学习分析
Md Shamshuzzoha1, Tazkia Tasnim Bahar Audry1, Md Jahangir Alam2
1Department of Computer Science and Engineering, United International University, United City, Madani Avenue, Badda, Dhaka 1212, Bangladesh.
Acta psychologica
|May 6, 2025
概括
这项研究引入了一种新的方法,用于使用社交媒体数据和机器学习来检测季节性情绪障碍 (SAD). 先进的技术实现了高精度,使得实时的心理健康监测.
科学领域:
- 计算精神病学是一种计算精神病学.
- 数字心理健康数字心理健康
- 机器学习在医疗保健中的应用.
背景情况:
- 季节性情绪障碍 (SAD) 是一种与季节性变化相关的复发性情绪障碍.
- 社交媒体为了解心理健康趋势提供了丰富的来源.
- 现有的SAD检测方法在数据和实时预测方面存在局限性.
研究的目的:
- 开发一种以数据为导向的方法,使用社交媒体准确检测季节性情绪障碍.
- 解决SAD研究中有限的多式联运数据集和阶级不平衡的挑战.
- 为实时社交媒体数据处理创建一个计算高效的模型.
主要方法:
- 策划了一个独特的社交媒体数据集,反映季节性模式.
- 应用合成少数群体过量采样技术 (SMOTE) 来缓解阶级不平衡.
- 评估了机器学习模型,包括随机森林,LGBM,XGBoost和KNN用于SAD检测.
主要成果:
- K-最近的邻居 (KNN) 获得了最高的训练准确率,达到97.87%.
- 随机森林,LGBM和XGBoost表现出强的表现.
- 计算效率被优化为实时可扩展性.
结论:
- 机器学习模型,特别是KNN,显示出从社交媒体上准确检测SAD的巨大潜力.
- SMOTE应用程序影响模型通用化,需要仔细实施.
- 这项研究通过社交媒体分析实现了可扩展的实时心理健康监测.
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