Abstract
Introduction: COVID-19 is an acute respiratory illness that directly affects the lungs It is much needed to predict the possibility of occurrence of COVID-19 based on their characteristics Objective: This paper studies the different machine learning classification algorithms to predict the COVID-19 recovered and deceased cases Methods: The k-fold cross-validation resampling technique is used to validate the prediction model Aim and The prediction scores of each algorithm are evaluated with performance metrics such as prediction accuracy, precision, recall, mean square error, confusion matrix, and kappa score For the preprocessed dataset, the k-nearest neighbour (KNN) classification algorithm produces 80 4 % of predication accuracy and 1 5 to 3 3 % of improved accuracy over other algorithms Results: The KNN algorithm predicts 92 % (true positive rate) of the deceased cases correctly, with 0 077% of misclassification Further, the KNN algorithm produces the lowest error rate as 0 19 on the prediction of accurate COVID-19 cases than the other algorithm Also, it produces the receiver operator characteristic curve with an output value of 82 % Conclusion: Based on the prediction results of various machine learning classification algorithms on the COVID-19 dataset, this paper shows that the KNN algorithm predicts COVID-19 possibilities well for the smaller (730 records) dataset than other algorithms © IJCRR
Highlights
Covid-19 [1,2,3] a disease which was caused due to a virus called coronavirus
The classifiers evaluated in this research work are Logistic Regression (LR), K-Neighbors Classifier (KNN), Decision Tree (DT), Support Vector Machines (SVM), and Multi-Layer Perceptron (MLP)
The 10-fold cross-validation method intends to reduce the bias of the prediction model [47]
Summary
Covid-19 [1,2,3] a disease which was caused due to a virus called coronavirus It became a global epidemic disease according to World Health Organisation (WHO). When the condition of the patient becomes worse with respiratory issues, the patient needs to be treated in intensive care unit with ventilation The mortality of this disease increases day by day and this disease becomes as a big threat to the human kind of entire world. Another study [5] was done for segmenting and quantifying the infection of CT images They used the CT images of chest and lung and they implemented it using deep learning technique.
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