用脑电图和人口学数据，99.9%准确率提前识别精神分裂症。
Insights into Schizophrenia: Leveraging Machine Learning for Early Identification via EEG, ERP, and Demographic Attributes
- 融合脑电事件相关电位与人口学特征，构建机器学习分类器。
- 在81人数据集上达到99.93%准确率，优于以往深度学习方法。
- 通过熵值分析识别关键特征，助力临床早期诊断决策。
本研究提出一种机器学习分类器，通过提取脑电图（EEG）数据中的事件相关电位（ERPs）特征及部分人口学变量，区分精神分裂症患者与健康对照组。数据来自在线数据库，共包含81名参与者（32名健康对照，49名精神分裂症患者）。经预处理后，模型准确率达99.930%，显著优于先前采用深度学习的研究。进一步分析通过逐项剔除特征或基于熵值递进剔除的方式，评估各特征对分类性能的贡献，以识别最具信息量的特征。
原文摘要 · Abstract (English)
The research presents a machine learning (ML) classifier designed to differentiate between schizophrenia patients and healthy controls by utilising features extracted from electroencephalogram (EEG) data, specifically focusing on event-related potentials (ERPs) and certain demographic variables. The dataset comprises data from 81 participants, encompassing 32 healthy controls and 49 schizophrenia patients, all sourced from an online dataset. After preprocessing the dataset, our ML model achieved an accuracy of 99.930%. This performance outperforms earlier research, including those that used deep learning methods. Additionally, an analysis was conducted to assess individual features' contribution to improving classification accuracy. This involved systematically excluding specific features from the original dataset one at a time, and another technique involved an iterative process of removing features based on their entropy scores incrementally. The impact of these removals on model performance was evaluated to identify the most informative features.
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