Early prediction of mental health issues among individuals is paramount for early diagnosis and treatment by mental health professionals. One of the promising approaches to achieving fully automated computer-based approaches for predicting mental health problems is via machine learning. As such, this study aims to empirically evaluate several popular machine learning algorithms in classifying and predicting mental health problems based on a given data set, both from a single classifier approach as well as an ensemble machine learning approach. The data set contains responses to a survey questionnaire that was conducted by Open Sourcing Mental Illness (OSMI). Machine learning algorithms investigated in this study include Logistic Regression, Gradient Boosting, Neural Networks, K-Nearest Neighbours, and Support Vector Machine, as well as an ensemble approach using these algorithms. Comparisons were also made against more recent machine learning approaches, namely Extreme Gradient Boosting and Deep Neural Networks. Overall, Gradient Boosting achieved the highest overall accuracy of 88.80% followed by Neural Networks with 88.00%. This was followed by Extreme Gradient Boosting and Deep Neural Networks at 87.20% and 86.40%, respectively. The ensemble classifier achieved 85.60% while the remaining classifiers achieved between 82.40 and 84.00%. The findings indicate that Gradient Boosting provided the highest classification accuracy for this particular mental health bi-classification prediction task. In general, it was also demonstrated that the prediction results produced by all of the machine learning approaches studied here were able to achieve more than 80% accuracy, thereby indicating a highly promising approach for mental health professionals toward automated clinical diagnosis.

中文翻译：

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用于心理健康预测的单一分类器与集成机器学习方法

个人心理健康问题的早期预测对于心理健康专业人员的早期诊断和治疗至关重要。实现基于计算机的完全自动化预测心理健康问题的方法之一是通过机器学习。因此，本研究旨在根据给定的数据集，从单一分类器方法和集成机器学习方法，对几种流行的机器学习算法进行实证评估，以对心理健康问题进行分类和预测。该数据集包含对 Open Sourcing Mental Illness (OSMI) 进行的调查问卷的回复。本研究中研究的机器学习算法包括逻辑回归、梯度提升、神经网络、K 最近邻和支持向量机，以及使用这些算法的集成方法。还与最近的机器学习方法进行了比较，即极端梯度提升和深度神经网络。总的来说，梯度提升达到了 88.80% 的最高整体准确率，其次是神经网络，达到 88.00%。其次是极端梯度提升和深度神经网络，分别为 87.20% 和 86.40%。集成分类器达到了 85.60%，而其余分类器达到了 82.40 到 84.00% 之间。研究结果表明，梯度提升为这个特定的心理健康双分类预测任务提供了最高的分类准确率。总的来说，还证明了这里研究的所有机器学习方法产生的预测结果能够达到 80% 以上的准确率，