This study presents a machine learning model based on the Naive Bayes classifier for predicting the level of depression in university students, the objective was to improve prediction accuracy using a machine learning model involving 70% training data and 30% validation data based on the Naive Bayes classifier, the collected data includes factors associated with depression from 519 university students, the results showed an accuracy of 78.03%, high sensitivity in detecting positive cases of depression, especially at moderate and severe levels, and significant specificity in correctly classifying negative cases, these findings highlight the effectiveness of the model in early detection and treatment of depression, benefiting vulnerable sectors and contributing to the improvement of mental health in the student population.

翻译：本研究提出一种基于朴素贝叶斯分类器的机器学习模型，用于预测大学生的抑郁程度。目标是通过包含70%训练数据和30%验证数据的朴素贝叶斯分类器机器学习模型提高预测准确率。收集的数据涵盖来自519名大学生的抑郁相关因素。结果显示，模型准确率为78.03%，在检测抑郁症阳性病例（尤其是中度和重度病例）方面具有高敏感性，且在正确分类阴性病例方面表现出显著特异性。这些发现凸显了该模型在抑郁症早期检测和治疗中的有效性，有利于保护弱势群体，并促进大学生群体心理健康的改善。