可靠的多类心理健康预测使用WiSARD歧视模型对不平衡的数据进行预测
1Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.
概括
这项研究引入了WiSARD分类器,用于准确预测多类精神障碍,在识别抑郁和焦虑等疾病方面表现优于其他模型,即使数据不平衡.
科学领域:
- 计算精神病学是一种计算精神病学.
- 机器学习用于医疗保健
背景情况:
- 机器学习 (ML) 对心理障碍的预测至关重要,有助于早期查和个性化护理.
- 挑战包括高维度,阶级不平衡,以及多类分类中的微妙心理特征.
研究的目的:
- 引入和评估一个可解释的,基于RAM的WiSARD分类器,用于多重障碍精神健康预测.
- 将WiSARD的性能与公开数据集上的已建立的ML模型进行比较.
主要方法:
- 一项回顾性研究使用了卡格尔精神障碍数据集 (637个完整的病例,29个特征).
- 使用10倍分层交叉验证对多层感知子,天真贝叶斯,DTNB,IB1和A1DE进行WiSARD测试.
- 性能指标包括精度,回忆,F测量,准确性,MCC,MAE和KS.
主要成果:
- WiSARD以98.27%的精度,0.983 F-测量,0.982 MCC和0.981 KS取得了卓越的性能.
- WiSARD对少数阶级的错误分类表现出更好的耐受性,解决了数据不平衡.
- 一项废除研究通过基于RAM的模式识别证实了WiSARD的可靠性和可解释性.
结论:
- WiSARD是一个有希望的,可解释的模型,用于预测多类精神障碍,特别是在不平衡的数据集.
- 结果仅限于单个非临床数据集与自我报告的数据,需要正式的精神病学验证.
关键词:
基于RAM的学习基于RAM的学习在WiSARD分类器.临床决策支持 临床决策支持不平衡的数据不平衡的数据.机器学习是机器学习.心理障碍预测 心理障碍预测多个类别的分类分类.心理诊断 心理诊断 是一种心理诊断.更多相关视频
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