Author: Rahul Kumar; Ridhi Arora; Vipul Bansal; Vinodh J Sahayasheela; Himanshu Buckchash; Javed Imran; Narayanan Narayanan; Ganesh N Pandian; Balasubramanian Raman
Title: Accurate Prediction of COVID-19 using Chest X-Ray Images through Deep Feature Learning model with SMOTE and Machine Learning Classifiers Document date: 2020_4_17
ID: 59ghorzf_22
Snippet: • Now, in the dataset, the number of data points for one class is very less when compared to the other class corresponding to the COVID-19 patients. Therefore, to balance the imbalanced data points, we use synthetic minority oversampling technique (SMOTE) 23, 24 . This algorithm creates an equal number of samples for each class. The above method was incorporated to ensure the smooth working of many machine learning algorithms like Logistic Regr.....
Document: • Now, in the dataset, the number of data points for one class is very less when compared to the other class corresponding to the COVID-19 patients. Therefore, to balance the imbalanced data points, we use synthetic minority oversampling technique (SMOTE) 23, 24 . This algorithm creates an equal number of samples for each class. The above method was incorporated to ensure the smooth working of many machine learning algorithms like Logistic Regression, Decision Tree, etc. which otherwise tends to be more biased towards a majority class. The algorithm mentioned above generates virtual data points between existing points of the minority class by using linear interpolation.
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