To search, Click below search items. |
| All Published Papers Search Service |
|
|
|
Title |
Classification of COVID-19 Disease: A Machine Learning Perspective |
|
Author |
Kinza Sardar |
| Citation |
Vol. 24 No. 3 pp. 107-112 |
|
Abstract |
Nowadays the deadly virus famous as COVID-19 spread all over the world starts from the Wuhan China in 2019. This disease COVID-19 Virus effect millions of people in very short time. There are so many symptoms of COVID19 perhaps the Identification of a person infected with COVID-19 virus is really a difficult task. Moreover it’s a challenging task to identify whether a person or individual have covid test positive or negative. We are developing a framework in which we used machine learning techniques..The proposed method uses DecisionTree, KNearestNeighbors, GaussianNB, LogisticRegression, BernoulliNB , RandomForest , Machine Learning methods as the classifier for diagnosis of covid ,however, 5-fold and 10-fold cross-validations were applied through the classification process. The experimental results showed that the best accuracy obtained from Decision Tree classifiers. The data preprocessing techniques have been applied for improving the classification performance. Recall, accuracy, precision, and F-score metrics were used to evaluate the classification performance. In future we will improve model accuracy more than we achieved now that is 93 percent by applying different techniques |
|
Keywords |
Covid-19, Machine Learning Techniques, Classification, Dimensionality Reduction |
|
URL |
