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- W4319998330 abstract "In today's world, fake news is the biggest and most challenging problem in the natural world environment. This type of fake news will create many problems in society, especially in the medical field. This research aims to detect automatic phoney news in the news. The following machine learning algorithm will help us to see phoney medical information based on dataset 1. KNN, 2. Naive Bayes, 3. Support Vector Machine, 4. BERT and 5. Decision tree, but the Decision tree will provide better accuracy. Findings: Performance measures such as accuracy, precision, recall, and f1-score showed 98.5% accuracy of our proposed Adaboost & Decision Tree algorithm. In this research work, we introduced and implemented the Proposed Ensembling (Adaboost & Decision tree) and achieved better accuracy. We collected and trained the dataset to identify misinformation in the medical area." @default.
- W4319998330 created "2023-02-11" @default.
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- W4319998330 date "2022-12-31" @default.
- W4319998330 modified "2023-10-16" @default.
- W4319998330 title "Fake News Detection in the Medical Field Using Machine Learning Techniques" @default.
- W4319998330 doi "https://doi.org/10.18280/ijsse.120608" @default.
- W4319998330 hasPublicationYear "2022" @default.
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