Effective asthma disease prediction using naive Bayes — Neural network fusion technique

Author

Aneja, S. ; Lal, S.

Author_Institution

JIIT, Noida, India

fYear

2014

fDate

11-13 Dec. 2014

Firstpage

137

Lastpage

140

Abstract

Asthma is a lung disease caused by the inflammation and narrowing of the airways that causes recurrent attacks of breathlessness and wheezing, and often can be life-threatening. Around 15-20 million people are suffering from asthma in India[1]. This paper aims at analyzing various data mining techniques for the prediction of asthma. The observations show that the fusion approach of naive bayes and neural network proved to be the best among classification algorithms in the diagnosis of asthma. This methodology is evaluated using 1024 raw data obtained from a city hospital. The proposed approach helps patients in their diagnosis of asthma.

Keywords

data mining; diseases; neural nets; patient diagnosis; pattern classification; sensor fusion; asthma diagnosis; asthma disease prediction; classification algorithm; data fusion; data mining; naive Bayes algorithm; neural network; Accuracy; Classification algorithms; Data mining; Diseases; Lungs; Neural networks; Prediction algorithms; Fusion Of Naïve Bayes and Neural Network Classifier; Naive Bayes Algorithm; Neural Network; asthma;

fLanguage

English

Publisher

ieee

Conference_Titel

Parallel, Distributed and Grid Computing (PDGC), 2014 International Conference on

Conference_Location

Solan

Print_ISBN

978-1-4799-7682-9

Type

conf

DOI

10.1109/PDGC.2014.7030730

Filename

7030730