基于机器学习的新冠疑似人员预测
Machine Learning-Based Prediction of Suspected Coronavirus Personnel

自2020年以来，新冠疫情的迅速扩散，对全世界的社会经济和公共卫生造成了十分严重的影响。如何快速准确地诊断和预测潜在的新冠病例是当前亟需解决的问题。机器学习技术具有处理大量数据、提高预测准确率等优势，在疫情防控中有广泛应用。本文基于机器学习算法，对疑似新冠人员的预测进行研究。通过随机函数生成有效病例数据，并利用SVM (Support Vector Machine)分类模型对其进行训练，经过多次实验，我们发现该模型能够准确地预测疾病，而且具有较高的可靠性。
Since 2020, the epidemic situation in COVID-19 has spread rapidly, which has had a very serious impact on social economy and public health all over the world. How to diagnose and predict potential COVID-19 cases quickly and accurately is an urgent problem at present. Machine learning technology has the advantages of processing large amounts of data and improving prediction accuracy, and has been widely used in epidemic prevention and control. Based on the machine learning algorithm, this paper studies the prediction of people suspected of COVID-19. The valid case data is generated by random functions and trained and tested using the SVM (Support Vector Machine) classification model, and after many experiments, we found that the model can accurately predict the disease and has high reliability.