Estimation and Prediction of Swine Flu Information using Speech Based Chatbot Model
Keywords:
Naive Bayes Classifier, DLSC classifier, Data mining, Swin flu DiseaseAbstract
In this research, we apply a complex chatbot model to determine the origin of the swine flu pandemic and develop an effective treatment strategy. The helpful patient's dataset collects a wealth of information. The lack of clarity in these depictions is often associated with the discovery of new diseases. The use of historical patient data to predict heart, lung, and other tumor infections is a promising new direction in medicine. Our research is based on a comprehensive strategy for gathering and using information about Swine Flu. As a result of this research, forecasting models and trouble spots have been identified. There was use of split classification that uses dynamic learning to make decisions. Data mining has a significant obstacle ahead of it in the shape of disease prediction. Here, we use a database of treatment patients to try to identify swine flu, the most rapidly spreading disease on the planet. Since swine flu is a respiratory illness, a comprehensive battery of diagnostic procedures must be performed on the patient. Advanced information mining methods could be used to fix this problem. The DLSC method has been used to the speech samples obtained from the patients via the cloud network. At long last, we're diagnosing utilizing data from speech samples gleaned from a training set. Chatbot technology has been used to discuss the swine flu and its treatment. Accuracy of 93.23%, sensitivity of 94.23%, recall of 92.12%, F1 score of 95.21%, and throughput of 90.34% are all achieved by this model, making it competitive with existing models.
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