Ilu Saratu Yusuf, Rajesh Prasad, Mohammed Hassan
African University of Science and Technology, Abuja, Nigeria.
Bayero University, Kano, Nigeria.
Inform Med Unlocked. 2022;31:100990. doi: 10.1016/j.imu.2022.100990. Epub 2022 Jun 3.
Severe acute respiratory syndrome coronavirus (SARS-COV) is a major family of viruses that cause infections in both animals and humans, including common cold, coronavirus disease (COVID-19), severe acute respiratory syndrome (SARS), and Middle East respiratory syndrome. This study primarily aims to predict the number of COVID-19 positive cases in 36 states of Nigeria using a long short-term memory (LSTM) algorithm of deep learning. The proposed approach employs K-means clustering to detect outliers and principal component analysis (PCA) to select important features from the dataset. The LSTM was chosen because of its non-linear characteristics to handle the dataset. As COVID-19 cases follow non-linear characteristics, LSTM is the most suitable algorithm for predicting their numbers. For comparison, several types of machine learning algorithms, such as naive Bayes, XG-boost, and SVM, were employed. After the comparison, LSTM was observed to be superior among all algorithms.
严重急性呼吸综合征冠状病毒（SARS-CoV）是一类主要的病毒，可在动物和人类中引发感染，包括普通感冒、冠状病毒病（COVID-19）、严重急性呼吸综合征（SARS）和中东呼吸综合征。本研究主要旨在使用深度学习的长短期记忆（LSTM）算法预测尼日利亚36个州的COVID-19阳性病例数。所提出的方法采用K均值聚类来检测异常值，并使用主成分分析（PCA）从数据集中选择重要特征。选择LSTM是因为其具有处理数据集的非线性特征。由于COVID-19病例具有非线性特征，LSTM是预测其病例数最合适的算法。为了进行比较，还采用了几种类型的机器学习算法，如朴素贝叶斯、XG-boost和支持向量机（SVM）。比较之后，发现LSTM在所有算法中表现最优。
