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首页> 外文会议>International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing >Classifying Serious Depression Based on Blood Test: A Machine Learning Approach
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Classifying Serious Depression Based on Blood Test: A Machine Learning Approach

机译：基于验血的严重抑郁症：机器学习方法

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摘要

As it is not possible to identify mental risk elements through basic physical examinations and even individuals suspected of having serious depression do not take mental examinations as much as physical examinations, it is necessary to specifically predict and analyze mental diseases through basic physical examinations alone. Therefore, in this study, a model capable of predicting severe depression through physical elements and individual environmental factors is created, and its accuracy, sensitivity, and specificity are analyzed. In particular, neural networks are utilized for the prediction of severe depression. The artificial neural network (ANN) model is used and the results are compared. The comparison of the results the ANN model using various optimization methods revealed that the severe depression prediction accuracy of the ANN model is 83.16%. In addition, the prediction accuracy of the machine learning algorithm for severe depression prediction is presented in detail by comparing the area under curve (AUG) results of the two models.
机译：由于不可能通过基本的体检甚至涉嫌患有严重抑郁症的个体来识别精神风险，并且甚至患有严重抑郁症的人都不服用身体检查，因此有必要通过仅通过基本体育检查来专门预测和分析精神疾病。因此，在本研究中，创建了一种能够通过物理元素和各个环境因素预测严重抑制的模型，分析了其准确性，灵敏度和特异性。特别地，神经网络用于预测严重抑郁症。使用人工神经网络（ANN）模型，并比较结果。结果比较ANN模型采用各种优化方法的比较显示，ANN模型的严重凹陷预测精度为83.16％。此外，通过比较两种模型的曲线（八八）结果的区域来详细介绍了严重凹陷预测的机器学习算法的预测精度。

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