The increasing prevalence of mental disorders among youth worldwide is one of society's most pressing issues. The proposed methodology introduces an artificial intelligence-based approach for comprehending and analyzing the prevalence of neurological disorders. This work draws upon the analysis of the Cities Health Initiative dataset. It employs advanced machine learning and deep learning techniques, integrated with data science, statistics, optimization, and mathematical modeling, to correlate various lifestyle and environmental factors with the incidence of these mental disorders. In this work, a variety of machine learning and deep learning models with hyper-parameter tuning are utilized to forecast trends in the occurrence of mental disorders about lifestyle choices such as smoking and alcohol consumption, as well as environmental factors like air and noise pollution. Among these models, the convolutional neural network (CNN) architecture, termed as DNN1 in this paper, accurately predicts mental health occurrences relative to the population mean with a maximum accuracy of 99.79%. Among the machine learning models, the XGBoost technique yields an accuracy of 95.30%, with an area under the ROC curve of 0.9985, indicating robust training. The research also involves extracting feature importance scores for the XGBoost classifier, with Stroop test performance results attaining the highest importance score of 0.135. Attributes related to addiction, namely smoking and alcohol consumption, hold importance scores of 0.0273 and 0.0212, respectively. Statistical tests on the training models reveal that XGBoost performs best on the mean squared error and R-squared tests, achieving scores of 0.013356 and 0.946481, respectively. These statistical evaluations bolster the models' credibility and affirm the best-fit models' accuracy. The proposed research in the domains of mental health, addiction, and pollution stands to aid healthcare professionals in diagnosing and treating neurological disorders in both youth and adults promptly through the use of predictive models. Furthermore, it aims to provide valuable insights for policymakers in formulating new regulations on pollution and addiction.

中文翻译：

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人工智能驱动的精神健康障碍分类方法的试点研究

全世界青少年中精神障碍的患病率日益上升，是社会最紧迫的问题之一。所提出的方法引入了一种基于人工智能的方法来理解和分析神经系统疾病的患病率。这项工作基于对城市健康倡议数据集的分析。它采用先进的机器学习和深度学习技术，与数据科学、统计学、优化和数学建模相结合，将各种生活方式和环境因素与这些精神障碍的发病率联系起来。在这项工作中，利用各种具有超参数调整的机器学习和深度学习模型来预测与吸烟和饮酒等生活方式选择以及空气和噪音污染等环境因素有关的精神障碍的发生趋势。在这些模型中，卷积神经网络 (CNN) 架构（本文中称为 DNN1）能够准确预测相对于总体平均值的心理健康事件，最大准确率达到 99.79%。在机器学习模型中，XGBoost技术的准确率为95.30%，ROC曲线下面积为0.9985，表明训练稳健。该研究还涉及为 XGBoost 分类器提取特征重要性分数，Stroop 测试性能结果获得了最高重要性分数 0.135。与成瘾相关的属性，即吸烟和饮酒，重要性得分分别为 0.0273 和 0.0212。对训练模型的统计测试表明，XGBoost 在均方误差和右-平方测试，得分分别为 0.013356 和 0.946481。这些统计评估增强了模型的可信度并确认了最佳拟合模型的准确性。拟议的心理健康、成瘾和污染领域的研究将有助于医疗保健专业人员通过使用预测模型迅速诊断和治疗青少年和成人的神经系统疾病。此外，它还旨在为政策制定者制定有关污染和成瘾的新法规提供宝贵的见解。