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.
翻译:机器学习算法被广泛归类为有监督的、不受监督的和强化的学习。 本文载有对我们实验研究工作的详细描述, 我们使用有监督的机器学习算法来建立我们的新科罗纳病毒爆发模型, 科罗纳病毒传播到全世界, 并造成许多死亡, 这是世界历史上最灾难性的大流行病之一。 人们在这个封闭的医学领域生存了体力和经济上的痛苦。 这项工作旨在更好地了解机器的深度预测、 共和和深层次学习模型的工作, 并在真实的数据集中实施。 在我们的工作中, 我们正在分析科罗纳病毒的当前趋势或模式, 然后通过使用机器学习算法来训练过去的科罗纳病毒病毒病毒病毒病毒病毒病毒的爆发模式, 从而进一步预测未来。 通过使用机器学习算法, 如线性回归、 质变模型、 K- 最接近的邻居、 决策树、 支持Victor 机器和随机森林算法来训练模型的模型。 在SVA 和 Riral Ral 模型中, 运行SV 和 Rmax 演算法的状态比 SV 更精确的模型要更好。