AI-driven early diagnosis of specific mental disorders: a comprehensive study.

IF 3.1 3区 工程技术 Q2 NEUROSCIENCES
Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-05-05 DOI:10.1007/s11571-025-10253-x
Firuze Damla Eryılmaz Baran, Meric Cetin
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引用次数: 0

Abstract

One of the areas where artificial intelligence (AI) technologies are used is the detection and diagnosis of mental disorders. AI approaches, including machine learning and deep learning models, can identify early signs of bipolar disorder, schizophrenia, autism spectrum disorder, depression, suicidality, and dementia by analyzing speech patterns, behaviors, and physiological data. These approaches increase diagnostic accuracy and enable timely intervention, which is crucial for effective treatment. This paper presents a comprehensive literature review of AI approaches applied to mental disorder detection using various data sources, such as survey, Electroencephalography (EEG) signal, text and image. Applications include predicting anxiety and depression levels in online games, detecting schizophrenia from EEG signals, detecting autism spectrum disorder, analyzing text-based indicators of suicidality and depression, and diagnosing dementia from magnetic resonance imaging images. eXtreme Gradient Boosting (XGBoost), light gradient-boosting machine (LightGBM), random forest (RF), support vector machine (SVM), K-nearest neighbor were designed as machine learning models, and convolutional neural networks (CNN), long short-term memory (LSTM) and gated recurrent unit (GRU) models suitable for the dataset were designed as deep learning models. Data preprocessing techniques such as wavelet transforms, normalization, clustering were used to optimize model performances, and hyperparameter optimization and feature extraction were performed. While the LightGBM technique had the highest performance with 96% accuracy for anxiety and depression prediction, the optimized SVM stood out with 97% accuracy. Autism spectrum disorder classification reached 98% accuracy with XGBoost, RF and LightGBM. The LSTM model achieved a high accuracy of 83% in schizophrenia diagnosis. The GRU model showed the best performance with 93% accuracy in text-based suicide and depression detection. In the detection of dementia, LSTM and GRU models have demonstrated their effectiveness in data analysis by reaching 99% accuracy. The findings of the study highlight the effectiveness of LSTM and GRU for sequential data analysis and their applicability in medical imaging or natural language processing. XGBoost and LightGBM are noted to be highly accurate ML tools for clinical diagnoses. In addition, hyperparameter optimization and advanced data pre-processing approaches are confirmed to significantly improve model performance. The results obtained with this study have revealed the potential to improve clinical decision support systems for mental disorders with AI, facilitating early diagnosis and personalized treatment strategies.

人工智能驱动的特定精神障碍早期诊断:一项综合研究。
人工智能(AI)技术应用的领域之一是精神障碍的检测和诊断。人工智能方法,包括机器学习和深度学习模型,可以通过分析语言模式、行为和生理数据来识别双相情感障碍、精神分裂症、自闭症谱系障碍、抑郁症、自杀和痴呆的早期迹象。这些方法提高了诊断的准确性,并使及时干预成为可能,这对有效治疗至关重要。本文对人工智能方法在精神障碍检测中的应用进行了全面的文献综述,这些方法使用了各种数据源,如调查、脑电图(EEG)信号、文本和图像。应用包括预测网络游戏中的焦虑和抑郁程度,从脑电图信号中检测精神分裂症,检测自闭症谱系障碍,分析基于文本的自杀和抑郁指标,以及从磁共振成像图像中诊断痴呆症。将eXtreme Gradient Boosting (XGBoost)、light Gradient - Boosting machine (LightGBM)、random forest (RF)、support vector machine (SVM)、K-nearest neighbor (k -近邻)等模型设计为机器学习模型,将适合该数据集的卷积神经网络(CNN)、长短期记忆(LSTM)和门控循环单元(GRU)模型设计为深度学习模型。采用小波变换、归一化、聚类等数据预处理技术优化模型性能,并进行超参数优化和特征提取。虽然LightGBM技术在焦虑和抑郁预测方面的准确率为96%,但优化后的SVM以97%的准确率脱颖而出。使用XGBoost、RF和LightGBM对自闭症谱系障碍的分类准确率达到98%。LSTM模型对精神分裂症的诊断准确率高达83%。GRU模型在基于文本的自杀和抑郁检测中表现最佳,准确率为93%。在痴呆症的检测中,LSTM和GRU模型在数据分析中已经证明了它们的有效性,准确率达到99%。该研究结果突出了LSTM和GRU在序列数据分析中的有效性,以及它们在医学成像或自然语言处理中的适用性。XGBoost和LightGBM被认为是用于临床诊断的高精度ML工具。此外,超参数优化和先进的数据预处理方法可以显著提高模型的性能。这项研究的结果表明,人工智能有可能改善精神障碍的临床决策支持系统,促进早期诊断和个性化治疗策略。
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来源期刊
Cognitive Neurodynamics
Cognitive Neurodynamics 医学-神经科学
CiteScore
6.90
自引率
18.90%
发文量
140
审稿时长
12 months
期刊介绍: Cognitive Neurodynamics provides a unique forum of communication and cooperation for scientists and engineers working in the field of cognitive neurodynamics, intelligent science and applications, bridging the gap between theory and application, without any preference for pure theoretical, experimental or computational models. The emphasis is to publish original models of cognitive neurodynamics, novel computational theories and experimental results. In particular, intelligent science inspired by cognitive neuroscience and neurodynamics is also very welcome. The scope of Cognitive Neurodynamics covers cognitive neuroscience, neural computation based on dynamics, computer science, intelligent science as well as their interdisciplinary applications in the natural and engineering sciences. Papers that are appropriate for non-specialist readers are encouraged. 1. There is no page limit for manuscripts submitted to Cognitive Neurodynamics. Research papers should clearly represent an important advance of especially broad interest to researchers and technologists in neuroscience, biophysics, BCI, neural computer and intelligent robotics. 2. Cognitive Neurodynamics also welcomes brief communications: short papers reporting results that are of genuinely broad interest but that for one reason and another do not make a sufficiently complete story to justify a full article publication. Brief Communications should consist of approximately four manuscript pages. 3. Cognitive Neurodynamics publishes review articles in which a specific field is reviewed through an exhaustive literature survey. There are no restrictions on the number of pages. Review articles are usually invited, but submitted reviews will also be considered.
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