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Epidemic Outbreak Prediction Using Artificial Intelligence


Affiliations
1 Analytic Labs Research Group, India
 

Intelligent Models for predicting diseases whether building a model to help the doctor or even preventing its spread in an area globally, is increasing day by day. Here we present a noble approach to predict the disease prone area using the power of Text Analysis and Machine Learning. Epidemic Search model using the power of the social network data analysis and then using this data to provide a probability score of the spread and to analyse the areas whether going to suffer from any epidemic spread-out, is the main focus of this work. We have tried to analyse and showcase how the model with different kinds of pre-processing and algorithms predict the output. We have used the combination of words-n grams, word embeddings and TFIDF with different data mining and deep learning algorithms like SVM, Naïve Bayes and RNN-LSTM. Naïve Bayes with TF-IDF performed better in comparison to others.

Keywords

Natural Language Processing, Text Mining, Text Analysis, Support Vector Machines, LSTM, Naive Bayes, Text Blob, Tweet Sentiment Analysis.
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  • Epidemic Outbreak Prediction Using Artificial Intelligence

Abstract Views: 426  |  PDF Views: 203

Authors

Nimai Chand Das Adhikari
Analytic Labs Research Group, India
Arpana Alka
Analytic Labs Research Group, India
Vamshi Kumar Kurva
Analytic Labs Research Group, India
S. Suhas
Analytic Labs Research Group, India
Hitesh Nayak
Analytic Labs Research Group, India
Rishav Kumar
Analytic Labs Research Group, India
Ashish Kumar Nayak
Analytic Labs Research Group, India
Sankalp Kumar Nayak
Analytic Labs Research Group, India
Vaisakh Shaj
Analytic Labs Research Group, India
Karthikeyan
Analytic Labs Research Group, India
Srikant Nayak
Analytic Labs Research Group, India

Abstract


Intelligent Models for predicting diseases whether building a model to help the doctor or even preventing its spread in an area globally, is increasing day by day. Here we present a noble approach to predict the disease prone area using the power of Text Analysis and Machine Learning. Epidemic Search model using the power of the social network data analysis and then using this data to provide a probability score of the spread and to analyse the areas whether going to suffer from any epidemic spread-out, is the main focus of this work. We have tried to analyse and showcase how the model with different kinds of pre-processing and algorithms predict the output. We have used the combination of words-n grams, word embeddings and TFIDF with different data mining and deep learning algorithms like SVM, Naïve Bayes and RNN-LSTM. Naïve Bayes with TF-IDF performed better in comparison to others.

Keywords


Natural Language Processing, Text Mining, Text Analysis, Support Vector Machines, LSTM, Naive Bayes, Text Blob, Tweet Sentiment Analysis.

References