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早期发现心理健康障碍,使用机器学习模型,使用行为和语音数据分析

Sunil Kumar Sharma^1,2, Ahmed Ibrahim Alutaibi³, Ahmad Raza Khan⁴

¹Department of Information Systems, College of Computer and Information Sciences, Majmaah University, 11952, Majmaah, Saudi Arabia. s.sharma@mu.edu.sa.

Scientific reports

|May 13, 2025

概括

这项研究介绍了NeuroVibeNet,这是一种用于使用语音和行为数据早期检测精神疾病的AI框架. 它达到99.06%的准确性,改善了诊断和患者支持.

科学领域:

人工智能的人工智能
计算精神病学是一种计算精神病学.
医疗保健中的机器学习

背景情况:

精神疾病是一个全球性的健康问题,需要早期诊断才能有效治疗.
手动诊断耗时,容易延误,增加自杀等风险.
人工智能为准确和及时检测心理健康障碍提供了潜在的潜力.

研究的目的:

开发一个新的AI框架,NeuroVibeNet,用于早期发现精神疾病.
利用多模式方法,整合语音和行为数据.
为了实现高准确度和可靠性,在区分正常和病态条件.

主要方法:

一个多模式框架预处理语音和行为数据集.
结合改进的随机森林 (IRF) 和光梯度增强机器 (LightGBM) 来获取行为数据.
集成混合支持矢量机 (SVM) 和K-最近邻居 (KNN) 语音数据,具有加权投票机制.

主要成果:

神经VibeNet框架表现出强的表现.
在分类正常和病态精神健康状况方面取得了99.06%的竞争准确率.
验证了多模式数据集成用于精神疾病检测的有效性.

结论:

拟议的AI框架使得可靠和早期发现精神疾病障碍成为可能.
多模式数据集成显著提高了诊断准确度.
通过及时干预,NeuroVibeNet显示了通过及时干预改善心理健康护理结果的希望.

关键词:

行为数据行为数据深度学习是一种深度学习.机器学习机器学习心理健康障碍心理健康障碍语音数据语音数据

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