The health sector collects a very large amount of data, hence the diagnostic process processes a very large and varied amount of data type which makes the process of analyzing these data very complicated, specifically the healthcare sector, mental health is very composed and varied by various data criteria. However, the forecast of health in modern life becomes very important. To this end, the proposed work aims to analyze patient data based on their represented symptoms, in order to help clinicians and mental health practitioners classify and refine the type of depression disorder “characterized” in patients intelligently, in order to make a relevant decision. In this context, the proposed system called CP-DDC is based on machine learning algorithms supervised more precisely by the random-forest algorithm. The dataset used in the case study contains 150 instances and 11 attributes, which define the different patient criteria, obtained from the Mohammed VI University Hospital Center of Marrakech “CHU.” The results of the experiment show that the proposed system offers the highest performance.

中文翻译：

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使用机器学习方法对抑郁症进行分类和预测的临床支持系统

卫生部门收集了非常大量的数据，因此诊断过程处理的数据类型非常庞大且多种多样，这使得分析这些数据的过程非常复杂，特别是医疗保健部门，心理健康非常多样化数据标准。然而，现代生活中的健康预测变得非常重要。为此，所提出的工作旨在根据患者表现的症状分析患者数据，以帮助临床医生和心理健康从业者智能地分类和细化患者“表征”的抑郁症类型，从而做出相关决策。在这种情况下，所提出的称为 CP-DDC 的系统基于机器学习算法，由随机森林算法更精确地监督。案例研究中使用的数据集包含 150 个实例和 11 个属性，它们定义了不同的患者标准，从马拉喀什穆罕默德六世大学医院中心“CHU”获得。实验结果表明，所提出的系统提供了最高的性能。