AN EVALUATION OF SWINE FLU (INFLUENZA A H3N2V) VIRUS PREDICTION USING DATA MINING AND CONVENTIONAL NEURAL NETWORK TECHNIQUES

Main Article Content

Pilla Srinivas, et. al.

Abstract

Nowadays, The health care commercial enterprise collects huge amounts of healthcare data which, unfortunately, are not “mined” to discover hidden information. Data mining plays a significant role in predicting diseases. The database report of medical patient is not more efficient, currently we made an Endeavour to detect the most widely spread disease in all over the world named Swine flu. Swine flu is a respiratory disease which has Numeral number of tests must be requisite from the patient for detecting a disease. Advanced data mining techniques gives us help to remedy this situation. In this work we describes about a prototype using data mining techniques, namely Naive Bayes Classifier. The Data mining is an emerging research trend which helps in finding accurate solutions in many fields. This paper highlights the various data mining technique and Convolution Neural Network used for predicting swine flu diseases.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

Article Details

How to Cite
et. al., P. S. . (2021). AN EVALUATION OF SWINE FLU (INFLUENZA A H3N2V) VIRUS PREDICTION USING DATA MINING AND CONVENTIONAL NEURAL NETWORK TECHNIQUES. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(4), 1377–1386. Retrieved from https://turcomat.org/index.php/turkbilmat/article/view/1214 (Original work published April 11, 2021)
Section
Research Articles