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- W4386989239 endingPage "100040" @default.
- W4386989239 startingPage "100040" @default.
- W4386989239 abstract "Infectious diseases have been posing to be a global threat in the recent times and are progressing from endemics to pandemics. The early detection and finding a better cure is one method to curb the disease and its transmission. The advent of machine learning (ML) demonstrate to be the ideal approach in early diagnosis of the disease. In the current review, the use of ML algorithm to monkeypox (MP) is highlighted. To extract useful information from the dataset, various models like CNN, DL, NLP, Naive Bayes, GRA-TLA, HMD, ARIMA, SEL, Regression analysis, and Twitter postings were built. These findings show that detection/classification, forecast and sentiment analysis were primarily analyzed. Furthermore this review will assist the researchers in understanding the latest implementation of ML on MP and to further progress in the field to discover potent therapeutics." @default.
- W4386989239 created "2023-09-24" @default.
- W4386989239 creator A5035717133 @default.
- W4386989239 date "2023-09-01" @default.
- W4386989239 modified "2023-10-18" @default.
- W4386989239 title "A Review on the Use of Machine Learning Techniques in Monkeypox Disease Prediction" @default.
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- W4386989239 doi "https://doi.org/10.1016/j.soh.2023.100040" @default.
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