The ongoing COVID-19 pandemic continues to pose significant challenges to global public health, despite the widespread availability of vaccines. Early detection of the disease remains paramount in curbing its transmission and mitigating its impact on public health systems. In response, this study delves into the application of advanced machine learning (ML) techniques for predicting COVID-19 infection probability. We conducted a rigorous investigation into the efficacy of various ML models, including XGBoost, LGBM, AdaBoost, Logistic Regression, Decision Tree, RandomForest, CatBoost, KNN, and Deep Neural Networks (DNN). Leveraging a dataset comprising 4000 samples, with 3200 allocated for training and 800 for testing, our experiment offers comprehensive insights into the performance of these models in COVID-19 prediction. Our findings reveal that Deep Neural Networks (DNN) emerge as the top-performing model, exhibiting superior accuracy and recall metrics. With an impressive accuracy rate of 89%, DNN demonstrates remarkable potential in early COVID-19 detection. This underscores the efficacy of deep learning approaches in leveraging complex data patterns to identify COVID-19 infections accurately. This study underscores the critical role of machine learning, particularly deep learning methodologies, in augmenting early detection efforts amidst the ongoing pandemic. The success of DNN in accurately predicting COVID-19 infection probability highlights the importance of continued research and development in leveraging advanced technologies to combat infectious diseases.

本研究针对COVID-19疫情中早期检测的挑战，探讨了多种先进机器学习模型在预测感染概率中的有效性。研究发现，深度神经网络（DNN）在准确性和召回率方面表现优异，准确率达到89%。这一结果强调了深度学习方法在提高疫情早期识别效率中的重要作用。

使用机器学习进行COVID-19概率预测：一种传染性方法