An Accurate Prediction of Disease Using Trapezoidal Long Short Term Recurrent Neural Network Model in Big Data

Abstract

;[‘.The eruption of Coronavirus 2019 (COVID‐19)has crashedday to day lives across the globe.The positive case count is increasing and India is in the top most position among the world countries. Human beings undergo chronic diseases with noidentification in time,thattransports increased load of disease to the society. This paper createsprognosticrepresentationwhich canforecast the positive count with increased accuracy. Regression‐based, Decision tree‐based, and Random forest‐based models are built on the data from China and are authenticated on sample of India. This is effectual and iscapablefor predicting the positive count with decreasedmistakes in future. This papergives an idea ofinfectionthreatforecast method toevaluates methodically furtherinfection threat for patients based on their present medical records using Trapezoidal Long Short Term Recurrent Neural Network model(TRAP-LSTM).This TRAP-LSTM follows multilayer structure with aggregation function and n–gram masking. The proposed TRAP-LSTM is compared with two state of art methods such as, Long Short Term Recurrent Neural Network (LST-RNN) and Gated Recurrent Units (GRU)interms of accuracy, precision, recall and harmonic score and hence the proposed method achieves90.2% of precision, 81.8% of recall,70.2% of accuracy and 46.4% of hamming score.