基于机器学习的分类精神分裂症患者的方法
Carmen Soria1,2, Yoel Arroyo3, Ana María Torres1
1Institute of Technology, University of Castilla-La Mancha, 16071 Cuenca, Spain.
Journal of clinical medicine
|July 14, 2023
概括
机器学习,特别是极端梯度增强 (XGB),使用脑电图 (EEG) 信号准确地分类精神分裂症. 这种自动化分析显示出高性能,有助于临床诊断.
科学领域:
- 神经科学是一个神经科学.
- 计算精神病学是一种计算精神病学.
- 医疗信息学 医疗信息学
背景情况:
- 精神分裂症显著影响对刺激的感知和反应.
- 脑电图 (EEG) 是一种有价值的,非侵入性工具,用于评估大脑疾病.
- 手动EEG分析耗时,需要自动化方法.
研究的目的:
- 开发和评估用于精神分裂症自动化EEG分析的机器学习 (ML) 方法.
- 评估 eXtreme渐变增强 (XGB) 算法的性能,用于对精神分裂症患者进行分类.
主要方法:
- 使用XGB算法开发了一种ML方法来分析EEG信号.
- 将XGB方法的性能与其他四种监督的ML算法进行了比较.
- 关键的绩效指标包括曲线下的面积 (AUC) 和准确性.
主要成果:
- 提出的基于XGB的方法实现了卓越的性能,AUC为0.94,准确度为0.94.
- 该系统在根据EEG数据对精神分裂症患者进行分类时表现出高准确性和稳定性.
- 在研究中评估的其他四种监督ML方法中,XGB的表现优于其他四种监督ML方法.
结论:
- 基于XGB的ML方法提供了一个非常准确和强大的方法,通过EEG检测精神分裂症.
- 这种自动化系统可以作为一个有价值的补充工具,以支持医院的临床诊断.
- 这些发现表明,可以提高诊断精神分裂症的效率和准确性.
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