Early Detection of Seasonal Outbreaks from Twitter Data Using Machine Learning Approaches

Amin, Samina; Uddin, M. Irfan; AlSaeed, Duaa; Khan, Atif; Adnan, Muhammad

doi:10.1155/2021/5520366

Cited by 25 publications

(6 citation statements)

References 33 publications

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“…Second, there is a limitation of insufficient data available, as COVID-19 has only had a period of 2 years of circulation compared to diseases such as influenza and norovirus that exhibit long-term epidemic patterns, which have been studied using ML to predict the start time of outbreaks in ( 34 , 35 ). To overcome this, we analyzed the pattern of COVID-19 transmission in Korea and successfully extracted features that were highly related to the labels listed in Table 1 .…”

Section: Discussionmentioning

confidence: 99%

Detection of COVID-19 epidemic outbreak using machine learning

Cho,

Park,

Choi

et al. 2023

Front. Public Health

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BackgroundThe coronavirus disease (COVID-19) pandemic has spread rapidly across the world, creating an urgent need for predictive models that can help healthcare providers prepare and respond to outbreaks more quickly and effectively, and ultimately improve patient care. Early detection and warning systems are crucial for preventing and controlling epidemic spread.ObjectiveIn this study, we aimed to propose a machine learning-based method to predict the transmission trend of COVID-19 and a new approach to detect the start time of new outbreaks by analyzing epidemiological data.MethodsWe developed a risk index to measure the change in the transmission trend. We applied machine learning (ML) techniques to predict COVID-19 transmission trends, categorized into three labels: decrease (L0), maintain (L1), and increase (L2). We used Support Vector Machine (SVM), Random Forest (RF), and XGBoost (XGB) as ML models. We employed grid search methods to determine the optimal hyperparameters for these three models. We proposed a new method to detect the start time of new outbreaks based on label 2, which was sustained for at least 14 days (i.e., the duration of maintenance). We compared the performance of different ML models to identify the most accurate approach for outbreak detection. We conducted sensitivity analysis for the duration of maintenance between 7 days and 28 days.ResultsML methods demonstrated high accuracy (over 94%) in estimating the classification of the transmission trends. Our proposed method successfully predicted the start time of new outbreaks, enabling us to detect a total of seven estimated outbreaks, while there were five reported outbreaks between March 2020 and October 2022 in Korea. It means that our method could detect minor outbreaks. Among the ML models, the RF and XGB classifiers exhibited the highest accuracy in outbreak detection.ConclusionThe study highlights the strength of our method in accurately predicting the timing of an outbreak using an interpretable and explainable approach. It could provide a standard for predicting the start time of new outbreaks and detecting future transmission trends. This method can contribute to the development of targeted prevention and control measures and enhance resource management during the pandemic.

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Section: Discussionmentioning

confidence: 99%

Detection of COVID-19 epidemic outbreak using machine learning

Cho,

Park,

Choi

et al. 2023

Front. Public Health

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“…The next stage is preprocessing, at the preprocessing stage, case folding, eliminating punctuation, eliminating numbers, tokenization, stopword removal, stemming and word normalization. In this study, the optimization stage in the preprocessing stage is prioritized (Bi et al, 2019), one of which is word normalization (Amin et al, 2021). Normalization of this word is so important and very crucial because in previous studies that discussed the same classification, the factor that affected the difference in accuracy between research was word normalization (Yennimar & Rizal, 2019).…”

Section: Resultsmentioning

confidence: 99%

Comparison of Naïve Bayes Algorithm and XGBoost on Local Product Review Text Classification

Hendrawan¹,

Utami²,

Hartanto³

2022

Edumatic

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Online reviews are critical in supporting purchasing decisions because, with the development of e-commerce, there are more and more fake reviews, so more and more consumers are worried about being deceived in online shopping. Sentiment analysis can be applied to Marketplace product reviews. This study aims to compare the two categories of Naïve Bayes and XGBoost by using the two vector spaces wod2vec and TFIDF. The methods used in this research are data collection, data cleaning, data labelling, data pre-processing, classification and evaluation. The data scraping process produced 25,581 data which was divided into 80% training data and 20% test data. The data is divided into two classes, namely good sentiment and bad sentiment. Based on the research that has been done, the combination of Word2vec + XGBoost F1 scores higher by 0.941, followed by TF-IDF + XGBoost by 0.940. Meanwhile, Naïve Bayes has an F1-Score of 0.915 with TF-IDF and 0.900 with word2vec. Classification using XGBoost proved to be able to classify unbalanced data better than Naïve Bayes.

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“…According to a previous study, prediction and forecast can be done using a decision tree. It can predict drug sentiment with low accuracy [15].…”

Section: Decision Treementioning

confidence: 99%

Drug Sentiment Analysis using Machine Learning Classifiers

Uddin¹,

Hafiz²,

Hossain³

et al. 2022

IJACSA

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In recent times, one of the most emerging subdimensions of natural language processing is sentiment analysis which refers to analyzing opinion on a particular subject from plain text. Drug sentiment analysis has become very significant in present times as classifying medicines based on their effectiveness through analyzing reviews from users can assist potential future consumers in gaining knowledge and making better decisions about a particular drug. The objective of this proposed research is to measure the effectiveness level of a particular drug. Currently most of the text mining researches are based on unsupervised machine learning methods to cluster data. When supervised learning methods are used for text mining, the usual primary concern is to classify the data into two classes. Lack of technical terms in similar datasets make the categorization even more challenging. The proposed research focuses on finding out the keywords through tokenization and lemmatization so that better accuracy can be achieved for categorizing the drugs based on their effectiveness using different algorithms. Such categorization can be instrumental for treating illness as well as improve one's health and well-being. Four machine learning algorithms have been applied for binary classification and one for multiclass classification on the drug review dataset acquired from the UCI machine learning repository. The machine learning algorithms used for binary classification are naive Bayes classifier, random forest, support vector classifier (SVC), and multilayer perceptron; among these machine learning algorithms, linear SVC was used for multiclass classification. Results obtained from these four classifier algorithms have been analyzed to evaluate their performances. The random forest has been proven to have the best performance among these four algorithms. However, multiclass classification was found to have low performance when applied to natural language processing. On the contrary, the applied linear SVC algorithm performed better for class 2 with AUC 0.82 in this research.

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Early Detection of Seasonal Outbreaks from Twitter Data Using Machine Learning Approaches

Cited by 25 publications

References 33 publications

Detection of COVID-19 epidemic outbreak using machine learning

Detection of COVID-19 epidemic outbreak using machine learning

Comparison of Naïve Bayes Algorithm and XGBoost on Local Product Review Text Classification

Drug Sentiment Analysis using Machine Learning Classifiers

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