AIRCC's International Journal of Computer Science and Information Technology

Nimai Chand Das Adhikari ¹, Arpana Alka ¹, Vamshi Kumar Kurva ¹, S. Suhas ¹, Hitesh Nayak ¹, Rishav Kumar ¹, Ashish Kumar Nayak ¹, Sankalp Kumar Nayak ¹, Vaisakh Shaj ¹, Karthikeyan ¹, Srikant Nayak ¹

Affiliations
1 Analytic Labs Research Group, IN

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.

Full Text