Dataset to identify lung disorders among COVID-19 patients using machine learning models

This data article explicit the breathing patterns of COVID-19 infected people and normal people with the openly available ultrasound videos by applying Non-Rigid Bodies (NRB) algorithm along with six different supervised machine learning classification models such as Decision Tree (DT), Logistic Regression (LR), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), k-Nearest Neighbour (kNN) and Random Forest (RF). The performance of each classification model has been measured depending on the correct and incorrect classified instances. The data in this article reveals that the SVM, kNN and RF classification models could classify COVID-19 infected people and normal people from ultrasound videos with high correction accuracy. Comparing to SVM, kNN and RF; the DT, LR and LDA models have only fewer correction accuracies. The data provided in this article indicate that this proposed method may be useful for the medical workers to differentiate the COVID-19 infected lungs for further diagnosis and also it can provide early warnings of COVID-19 infections among the people who have mild conditions. Moreover, this method can be suggested for controlling the widespread of the Coronavirus. Keywords: COVID-19; Lung; Non-Rigid Bodies algorithm; Ultrasound videos; Decision Tree; Logistic Regression; Linear Discriminant Analysis; Support Vector Machine; k-Nearest Neighbour; Random Forest. *Ethics approval and consent for publication Approval to conduct this research was obtained from the ‘Research Innovation Consultancy and Extensions (RICE)’ board of the author’s University system. Informed consent was obtained from all the patients for using their images/videos or other clinical information relating to this research. The consent for publication was obtained from the author’s University system. Enclosure: 1) Supplementary file with data and charts. 2) Ultrasound video set of COVID-19 and Normal Patients.