Baran Firuze Damla Eryılmaz, Cetin Meric
Department of Computer Engineering, Pamukkale University, 20160 Denizli, Turkey.
Cogn Neurodyn. 2025 Dec;19(1):70. doi: 10.1007/s11571-025-10253-x. Epub 2025 May 5.
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（AI）技术的应用领域之一是精神障碍的检测与诊断。包括机器学习和深度学习模型在内的人工智能方法，能够通过分析语音模式、行为和生理数据，识别双相情感障碍、精神分裂症、自闭症谱系障碍、抑郁症、自杀倾向和痴呆症的早期迹象。这些方法提高了诊断准确性，并能实现及时干预，这对有效治疗至关重要。本文对使用各种数据源（如调查、脑电图（EEG）信号、文本和图像）应用于精神障碍检测的人工智能方法进行了全面的文献综述。应用包括预测网络游戏中的焦虑和抑郁水平、从EEG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倾向和抑郁指标，以及从磁共振成像图像中诊断痴呆症。极端梯度提升（XGBoost）、轻量级梯度提升机（LightGBM）、随机森林（RF）、支持向量机（SVM）、K近邻被设计为机器学习模型，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）模型被设计为深度学习模型。使用小波变换、归一化、聚类等数据预处理技术来优化模型性能，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表现最佳，准确率达96%，但优化后的SVM以97%的准确率脱颖而出。使用XGBoost、RF和LightGBM进行自闭症谱系障碍分类的准确率达到了98%。LSTM模型在精神分裂症诊断中达到了83%的高准确率。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%。在痴呆症检测中，LSTM和GRU模型通过达到99%的准确率证明了它们在数据分析中的有效性。该研究结果突出了LSTM和GRU在序列数据分析方面的有效性及其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机器学习工具。此外，超参数优化和先进的数据预处理方法被证实能显著提高模型性能。本研究获得的结果揭示了利用人工智能改善精神障碍临床决策支持系统的潜力，有助于早期诊断和个性化治疗策略。
