Utilizing Advanced Artificial Intelligence for Early Detection of Epidemic Outbreaks through Global Data Analysis
Keywords:
Artificial Intelligence, Epidemic Prediction, TF-IDF, SVM (Support Vector Machine), Naive Bayes, Social NetworkAbstract
Artificial Intelligence (AI) is increasingly becoming a pivotal tool in disease prediction, aiding in both medical diagnostics and outbreak containment. This study presents a novel approach to forecasting disease-prone areas using Text Analysis and Machine Learning, focusing on the power of social network data to anticipate epidemic outbreaks. We have developed an epidemic search model that utilizes a combination of data pre-processing techniques and diverse algorithms to analyse the likelihood and potential locations of outbreaks. Our methodology integrates Support Vector Machine (SVM), Naive Bayes, and Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM) models, along with advanced text processing methods such as word-n-grams, word embedding, and Term Frequency-Inverse Document Frequency (TF-IDF). In our findings, the integration of Naive Bayes with TF-IDF emerged as the most effective technique, showcasing superior predictive capabilities. This research not only demonstrates the feasibility of using AI in epidemiological predictions but also underscores the potential of social network data in enhancing public health responses.
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