[论文解读] A Benchmark Study by using various Machine Learning Models for Predicting Covid-19 trends

D. Kamelesun, R. Saranya+1 more|arXiv (Cornell University)|Jan 26, 2023

COVID-19 diagnosis using AIMedicine被引用 3

一句话总结

本基准研究评估了多种机器学习模型——线性回归与多项式回归、K近邻（KNN）、支持向量机（SVM）、决策树、随机森林、集成方法（投票、装袋、堆叠）以及深度学习（人工神经网络，ANN）——在真实世界COVID-19数据集上的表现，以预测确诊病例的未来趋势。随机森林和集成模型表现优于其他模型，而SVR和套索回归表现较差，原因在于难以分离数据中复杂的非线性模式。

ABSTRACT

Machine learning and deep learning play vital roles in predicting diseases in the medical field. Machine learning algorithms are widely classified as supervised, unsupervised, and reinforcement learning. This paper contains a detailed description of our experimental research work in that we used a supervised machine-learning algorithm to build our model for outbreaks of the novel Coronavirus that has spread over the whole world and caused many deaths, which is one of the most disastrous Pandemics in the history of the world. The people suffered physically and economically to survive in this lockdown. This work aims to understand better how machine learning, ensemble, and deep learning models work and are implemented in the real dataset. In our work, we are going to analyze the current trend or pattern of the coronavirus and then predict the further future of the covid-19 confirmed cases or new cases by training the past Covid-19 dataset by using the machine learning algorithm such as Linear Regression, Polynomial Regression, K-nearest neighbor, Decision Tree, Support Vector Machine and Random forest algorithm are used to train the model. The decision tree and the Random Forest algorithm perform better than SVR in this work. The performance of SVR and lasso regression are low in all prediction areas Because the SVR is challenging to separate the data using the hyperplane for this type of problem. So SVR mostly gives a lower performance in this problem. Ensemble (Voting, Bagging, and Stacking) and deep learning models(ANN) also predict well. After the prediction, we evaluated the model using MAE, MSE, RMSE, and MAPE. This work aims to find the trend/pattern of the covid-19.

研究动机与目标

• 评估多种机器学习模型在预测COVID-19确诊病例未来趋势方面的有效性。
• 比较传统监督学习模型（如线性回归、SVM、决策树）与集成和深度学习方法的性能。
• 识别在真实世界、非线性时间序列疫情数据上表现最佳的模型。
• 通过标准回归指标（MAE、MSE、RMSE、MAPE）在不同预测场景下评估模型性能。

提出的方法

• 本研究使用历史COVID-19病例数据作为输入，训练监督学习机器学习模型。
• 评估的模型包括线性回归、多项式回归、K近邻（KNN）、支持向量回归（SVR）、决策树、随机森林以及集成技术（投票、装袋、堆叠）。
• 同时实现前馈人工神经网络（ANN）作为深度学习基线模型。
• 模型训练与预测基于确诊病例的时间序列数据，超参数经过调优以实现最佳性能。
• 通过MAE、MSE、RMSE和MAPE对性能进行定量评估，以比较各模型的预测准确性。
• 基准测试包括单个模型评估与集成堆叠，以提升泛化能力和鲁棒性。

实验结果

研究问题

• RQ1 在多种数据集上，哪种机器学习模型能对未来的COVID-19确诊人数提供最准确的预测？
• RQ2 传统模型如SVR和套索回归在捕捉疫情数据中的非线性趋势方面，与集成和深度学习模型相比表现如何？
• RQ3 集成技术（如装袋、堆叠）在多大程度上提升了相比单个模型的预测性能？
• RQ4 为何SVR和套索回归在此项特定的时间序列预测任务中表现不佳？

主要发现

• 随机森林和决策树模型在所有测试模型中达到了最高的预测准确性。
• 如投票、装袋和堆叠等集成方法表现出色，优于单个模型。
• 支持向量回归（SVR）表现持续偏低，原因在于难以分离数据中复杂的非线性模式。
• 套索回归也表现出较差性能，可能由于其对高维或噪声时间序列数据的敏感性。
• 人工神经网络（ANN）表现良好，表明深度学习在疫情预测中具有潜力。
• 评估指标（MAE、MSE、RMSE、MAPE）证实，随机森林和集成模型在所有指标中均具有最低的误差率。